The highly interconnected contemporary world is faced with an immense range of serious challenges in statistical learning, engineering and information sciences which make the development of statistical and stochastic methods for complex estimation problems and decision making critical. The most significant challenges arise in risk analysis, in environmental and statistical analysis of massive data sets, as well as in defense systems. From both the numerical and the theoretical viewpoints, there is a need for unconventional statistical and stochastic methods that go beyond the current frontier of knowledge.

Our approach to this interdisciplinary challenge is based on
recent developments in statistics and stochastic computational methods. We propose a work
programme which will lead to significant breakthroughs in fundamental and applied mathematical research,
as well as in advanced engineering and information sciences with industrial applications with a particular focus on defence applications, in collaboration with Naval Group.

Many real-world systems and processes are dynamic and essentially random. Examples can be found in many areas like communication and information systems, biology, geophysics, finance, economics, production systems, maintenance, logistics and transportation. These systems require dynamic and stochastic mathematical representations with discrete and/or continuous state variables in possibly infinite dimensional space. Their dynamics can be modeled in discrete or continuous time according to different time scales and are governed by different types of processes such as stochastic differential equations, piecewise deterministic processes, jump-diffusion processes, branching and mean field type interacting processes, reinforced processes and self-interacting Markov processes, to name a few. Our interdisciplinary project draws knowledge from information science, signal processing, control theory, statistics and applied probability including numerical and mathematical analysis. The idea is to work across these scientific fields in order to enhance their understandings and to offer an original theory or concept.

Our group mainly focuses on the development of advanced statistical and probabilistic methods for the analysis and the control of complex stochastic systems, as outlined in the following three topics.

These three items are by no means independent.

Solving such global optimization problems remain an open problem and is recognized in the literature as a very difficult challenge to meet.

One of the fundamental challenges we will address is to develop estimation/calibration and optimal control techniques related to general classes of stochastic processes in order to deal with real-world problems. Our research results will combine, mathematical rigour (through the application of advanced tools from probability, statistics, measure theory, functional analysis and optimization) with computational efficiency (providing accurate and applicable numerical methods with a refined analysis of the convergence). Thus, the results that we will obtain in this research programme will be of interest to researchers in the fields of stochastic modeling, statistics and control theory both for the theoretical and the applied communities. Moreover, the topics studied by Naval Group, such as target detection, nonlinear filtering, multi-object tracking, trajectory optimization and navigation systems, provide a diverse range of application domains in which to implement and test the methodologies we wish to develop.

The final goal is to develop a series of reliable and robust softwares dedicated to statistical and stochastic learning, as well as automated decision and optimal control processes. The numerical codes are required to be both accurate and fast since they are often elements of real time estimation and control loops in automation systems. In this regard, the research topics proposed by Naval Group will provide a natural framework for testing the efficiency and robustness of the algorithms developed by the team.

From our point of view, this collaboration between the INRIA project team and Naval Group offers new opportunities and strategies to design advanced cutting-edge estimation and control methodologies.

The types of learning and control methodologies developed by the team differ in their approach as well as in the problems that they are intended to solve. They can be summarised by the following three sets of interdependent methodologies.

All team members of the project work at the interface of the these three areas. This joint research project between INRIA and Naval Group is a natural and unprecedented opportunity to embrace and push the frontiers of the applied and theoretical sides of these research topics in a common research team.

Despite some recent advances, the design and the mathematical analysis of statistical and stochastic learning tools, as well as automated decision processes, is still a significant challenge. For example, since the mid-1970s nonlinear filtering problems and stochastic optimal control problems with partial observations have been the subject of several mathematical studies, however very few numerical solutions have been proposed in the literature.

Conversely, since the mid-1990s, there has been a virtual explosion in the use of stochastic particle methods as powerful tools in real-word applications of Monte Carlo simulation; to name a few, particle filters, evolutionary and genetic algorithms and ensemble Kalman filters. Most of the applied research in statistics, information theory and engineering sciences seems to be developed in a completely blind way with no apparent connections to the mathematical counterparts.

This lack of communication between the fields often produces a series of heuristic techniques often tested on reduced or toy models. In addition, most of these methodologies do not have a single concrete industrial application nor do they have any connection with physical problems.

As such, there exists a plethora of open mathematical research problems related to the analysis of statistical learning and decision processes. For instance, a variety of theoretical studies on particle algorithms, including particle filters and sequential Monte Carlo models are often based on ad-hoc and practically unrealistic assumptions for the kinds of complex models that are increasingly emerging in applications.

The aim of this project is to fill these gaps with an ambitious programme at the intersection of probability, statistics, engineering and information sciences.

One key advantage of the project is its interdisciplinary nature. Combining techniques from pure and applied mathematics, applied probability and statistics, as well as computer science, machine learning, artificial intelligence and advanced engineering sciences enables us to consider these topics holistically, in order to deal with real industrial problems in the context of risk management, data assimilation, tracking applications and automated control systems. The overarching aim of this ambitious programme is to make a breakthrough in both the mathematical analysis and the numerical aspects of statistical learning and stochastic estimation and control.

Fundamentally, our team is not driven by a single application. The reasons are three-fold. Firstly, the robustness and transferability of our approaches means that the same statistical or stochastic learning algorithms can be used in a variety of application areas. On the other hand every application domain offers a series of different perspectives that can be used to improve the design and performances of our techniques and algorithms. Last but not least, industrial applications, including those that arise in defence, require specific attention. As such, we use a broad set of stochastic and statistical algorithms to meet these demands.

This research programme is oriented towards concrete applications with significant potential industrial transfers on three central problems arising in engineering and information and data sciences, namely, risk management and uncertainty propagation, process automation, and data assimilation, tracking and guidance. Our ultimate goal is to bring cutting edge algorithms and advanced statistical tools to industry and defence. The main application domains developed by the team are outlined below:

The main objectives and challenges related to the three application domains discussed above will be developed in section . The latter application domain will be developed in collaboration with Naval Group. The reader is refereed to section for a description of this collaboration and to sections and for the theoretical aspects that will be carried out by the team in relation to these topics. Specific details on the particular techniques used to tackle the estimation and tracking problems in the context of the collaboration with Naval Group will remain confidential.

This section describes the different challenges we intend to address in the theoretical and numerical aspects of statistical/stochastic learning and optimal control. It will be difficult to convey the full complexity of the various topics and to provide a complete overview through a detailed timetable. Nevertheless, we will explain our motivation and why we think it is imperative to address these challenges. We will also highlight the technical issues inherent to these challenges, as well as the difficulties we might expect.

We are confident that the outcomes of this scientific project will lead to significant breakthroughs in statistical/stochastic learning and optimal control
with a special emphasis on applications in the defence industry in collaboration with Naval Group.
In this respect, we would like to quote Hervé Guillou, CEO of Naval Group, on the occasion of the signing of the partnership agreement between INRIA and Naval Group on December 10, 2019: ”This partnership will enable Naval Group to accelerate its innovation process in the fields of artificial intelligence, intelligence applied to cyber and signal processing. This is a necessity given the French Navy's need for technological superiority in combat and the heightened international competition in the naval defence field...”

One of our greatest achievements would undoubtedly be to meet these challenges with Naval Group, particularly those related to the fields of statistical/stochastic learning and control. We could not dream of a better outcome for our project.

Permanent researchers: M. Chavent, P. Del Moral, F. Dufour, A. Genadot, E. Horton, P. Legrand, J. Saracco.

Regarding statistical learning, some of the objectives of the team is to develop dimension reduction models, data visualization, non-parametric estimation methods, genetic programming and artificial evolution. These models/methodologies provide a way to understand and visualize the structure of complex data sets. Furthermore, they are important tools in several different areas of research, such as data analysis and machine learning, that arise in many applications in biology, genetics, environment and recommendation systems. Of particular interest is the analysis of classification and clustering approaches and semi-parametric modeling that combines the advantages of parametric and non-parametric models, amongst others. One major challenge is to tackle both the complexity and the quantity of data when working on real-world problems that emerge in industry or other scientific fields in academia. Of particular interest is to find ways to handle high-dimensional data with irrelevant and redundant information.

Another challenging task is to take into account successive arrivals of information (data stream) and to dynamically refine the implemented estimation algorithms, whilst finding a balance between the estimation precision and the computational cost. One potential method for this is to project the available information into suitably chosen lower dimensional spaces.

For regression models, sliced inverse regression (SIR) and related approaches have proven to be highly efficient methods for modeling the link between a dependent variable (which can be multidimensional) and multivariate covariates in several frameworks (data stream, big data, etc.). The underlying regression model is semi-parametric (based on a single index or on multiple indices that allow dimension reduction). Currently, these models only deal with quantitative covariates. One of the team’s goals is to extend these regression models to mixed data, i.e. models dealing with quantitative and categorical covariates. This generalization would allow one to propose discriminant analysis to deal with mixed data. Extension of sparse principal component analysis (PCA) to mixed data is also another challenge. One idea is to take inspiration from the underlying theory and method of recursive SIR and SIR approaches for data stream in order to adapt them to commonly used statistical methods in multivariate analysis (PCA, discriminant analysis, clustering, etc.). The common aim of all these approaches is to estimate lower dimensional subspaces whilst minimizing the loss of statistical information. Another important aspect of data stream is the possible evolution in time of the underlying model: we would like to study break(s) detection in semi-parametric regression model, the breakdown being susceptible to appear in the parametric part or in the functional part of the regression model. The question of selecting covariates in regression modelling when we deal with big data is a fundamental and difficult problem. We will address this challenge using genetic programming and artificial evolution. Several directions are possible: for instance, improve, via genetic algorithms, the exploration of the covariate space in closest submodel selection (CSS) method or study optimization problems that simultaneously take into account variable selection, efficiency of estimation and interpretability of the model. Another important question concerns the detection of outliers that will disturb the estimation of the model, and this is not an obvious problem to deal with when working with large, high dimensional data.

In multivariate data analysis, an objective of the team is to work on a new formulation/algorithm for group-sparse block PCA since it is always important to take into account group information when available. The advantage of the group-sparse block PCA is that, via the selection of groups of variables (based on the synthetic variables), interpretability of the results becomes easier. The underlying idea is to address the simultaneous determination of group-sparse loadings by block optimization, and the correlated problem of defining explained variance for a set of non-orthogonal components. The team is also interested in clustering of supervised variables, the idea being to construct clusters made up of variables correlated with each other, which are either well-linked or not-linked to the variable to be explained (which can be quantitative or qualitative).

Another way to study the links between variables is to consider conditional quantiles instead of conditional expectation as is the case in classical regression models. Indeed, it is often of interest to model conditional quantiles, particularly in the case where the conditional mean fails to take into account the impact of the covariates on the dependent variable. Moreover, the quantile regression function provides a much more comprehensive picture of the conditional distribution of a dependent variable than the conditional mean function. The team is interested in the non parametric estimation of conditional quantile estimation. New estimators based on quantization techniques have been introduced and studied in the literature for univariate conditional quantiles and multivariate conditional quantiles. However, there are still many open problems, such as combining information from conditional quantiles of different orders in order to refine the estimation of a conditional quantile of a given order.

Another topic of interest is genetic programming (GP) and Artificial Evolution. GP is an evolutionary computation paradigm for automatic program induction. GP has produced impressive results but there are still some practical limitations, including its high computational cost, overfitting and excessive code growth. Recently, many researchers have proposed fitness-case sampling methods to overcome some of these problems, with mixed results in several limited tests. Novelty Search (NS) is a unique approach towards search and optimization, where an explicit objective function is replaced by a measure of solution novelty. While NS has been mostly used in evolutionary robotics, the team would like to explore its usefulness in classic machine learning problems.

Another important objective of the team is to implement new R (Matlab/Python) packages or to enrich those existing in the literature with the methods we are going to develop in order to make them accessible to the scientific community.

With respect to our statistical learning research program, the objectives of the team can be divided into mid- and long-term works. Mid-term objectives focus on sparsity in SIR (via soft thresholding for instance) and group-sparse block PCA, the underlying idea being to make the selection of variables or blocks of variables in the regression model or in the data. Taking into account multi-block data in regression models via data-driven sparse partial least squares is also at the heart of our concerns. Coupling genetic algorithms and artificial evolution with statistical modeling issues is also planned. The team has several long-term projects associated with the notion of data stream. Many theoretical and practical problems arise from the possible evolution of the information contained in the data: break detection in the underlying model, balance between precision and computational cost. Another scientific challenge is to extend certain approaches such as SIR to the case of mixed data by incorporating the information provided by the qualitative variables in the associated low dimensional subspaces. Moreover, the team has already worked on clustering of variables for mixed data and the clustering of supervised variables is now planned. Finally the idea of combining information from conditional quantiles of different orders in order to refine the estimation of a given order conditional quantile is still relevant today. It should be noted that other research themes may appear or become a priority depending on the academic or industrial collaborations that may emerge during the next evaluation period.

Some topics of the INRIA project teams (STATIFY, CELESTE, MODAL, SEQUEL, CLASSIC) are close to the ASTRAL objectives such as non parametric view of high dimensional data, statistical/machine learning, model selection, clustering, sequential learning algorithms, or multivariate data analysis for complex data. While certain ASTRAL objectives are similar to those of these teams, our approaches are significantly different. For example, in multivariate data analysis of complex data including clustering, our team mainly focuses on a geometric approach for mixed data. We also consider the case of successive arrivals of information in SIR both from the theoretical and numerical point of view. Currently there is no direct competition between our team and other INRIA project teams. However, interactions between ASTRAL and other INRIA teams exist. For instance, ASTRAL and STATIFY collaborations are fruitful with common publications, in particular with S. Girard (STATIFY project team).

In the field of multivariate data analysis, the team have interesting discussions with Agrocampus Ouest (Rennes, France) and with H.A.L. Kiers (Groningen University) on a mixed data approach for dimension reduction. Conditional and regression quantiles are very active research fields in France (University of Toulouse, Toulouse School of Economics, University of Montpellier) and around the world (ULB, Belgium; University of Illinois Urbana-Champaign, USA; Open University, UK; Brunel University, UK). The ASTRAL team has for the last four-year period collaborated with D. Paindaveine (ULB, Belgium). In the dimension reduction framework, there is a large international community in Europe, America or Asia working on SIR and related methods. However, to our knowledge, the ASTRAL team was the first to introduce importance of variables and recursive methods in SIR, and the first to adapt the SIR approach to data stream.

Permanent researchers: M. Chavent, P. Del Moral, F. Dufour, A. Genadot, E. Horton, D. Laneuville, P. Legrand, A. Nègre, J. Saracco, H. Zhang.

Stochastic particle methodologies have become one of the most active intersections between pure and applied probability theory, Bayesian inference, statistical machine learning, information theory, theoretical chemistry, quantum physics, financial mathematics, signal processing, risk analysis, and several other domains in engineering and computer sciences.

Since the mid-1990s, rapid developments in computer science, probability and statistics have led to new generations of interacting particle learning/sampling type algorithms, such as:

Particle and bootstrap filters, sequential Monte Carlo methods, self-interacting and reinforced learning schemss,
sequentially interacting Markov chain Monte Carlo, genetic type search algorithms, island particle models, Gibbs cloning search techniques, interacting simulated annealing algorithms, importance sampling methods, branching and splitting particle algorithms, rare event simulations, quantum and diffusion Monte Carlo models, adaptive population Monte Carlo sampling models, Ensemble Kalman filters and interacting Kalman filters.

Since computations are nowadays much more affordable, the aforementioned particle methods have become revolutionary for solving complex estimation and optimization problems arising in engineering, risk analysis, Bayesian statistics and information sciences. The books , , , provide a rather complete review on these application domains.

These topics have constituted some of the main research axes of several of the ASTRAL team members since the beginning of the 1990s. To the best of our knowledge, the first rigorous study on particle filters and the convergence of genetic algorithms as the size of the population tends to infinity seems to be the article , published in 1996 in the journal Markov Processes and Related Fields. This paper has opened an avenue of research questions in stochastic analysis and particle methods applications. The uniform convergence of particle filters and ensemble Kalman filters with respect to the time horizon was first seen in , , and in the more recent article . The first use of particle algorithms and Approximated Bayesian Computation type methodologies in nonlinear filtering seems to have started in . Last but not least, the development of sequential Monte Carlo methodology in statistics was introduced in the seminal article .

Despite some recent advances, the mathematical foundation and the design and the numerical analysis of stochastic particle methods is still a significant challenge. For instance, particle filter technology is often combined with Metropolis-Hastings type techniques, or with Expectation Maximization type algorithms. The resulting algorithms are intended to solve high dimensional hidden Markov chain problems with fixed parameters. In this context (despite some recent attempts) the refined convergence analysis of the resulting particle algorithms, including exponential concentration estimates, remains to be developed.

Last but not least, the expectations of their performances are
constantly rising in a variety of application domains. These particle methodologies are now expected to deal with increasingly sophisticated models in high dimensions, whilst also allowing for the variables to evolve at different scales.
The overarching aim of this aspect of the programme is to make a breakthrough in both the mathematical analysis and the numerical simulation of stochastic and interacting particle algorithms.

Today, partly because of the emergence of new mean field simulation methodologies and partly because of the importance of new and challenging high-dimensional problems arising in statistical machine learning, engineering sciences and molecular chemistry, we are observing the following trends:

From a purely probabilistic point of view, the fundamental and the theoretical aspects of our research projects are essentially based on
the stochastic analysis of the following three classes of interacting stochastic processes: Spatial branching processes and mean-field type interacting particle systems, reinforced and self-interacting processes, and finally random tree based search/smoothing learning processes.

The first class of particle models includes interacting jump-diffusions, discrete generation models, particle ensemble Kalman filters and evolutionary algorithms. This class of models refers to mean field type interaction processes with respect to the occupation measure of the population. For instance genetic-type branching-selection algorithms are built on the following paradigm: when exploring a state space with many particles, we duplicate better fitted individuals while particles with poor fitness die. The selection is made by choosing randomly better fitted individuals in the population. Our final aim is to develop a complete mean-field particle theory combining the stability properties of the limiting processes as the size of the system tends to infinity with the performance analysis of these particle sampling tools.

The second class of particle models refers to mean field type interaction processes with respect to the occupation measure of the past visited sites. This type of reinforcement is observed frequently in nature and society, where "beneficial" interactions with the past history tend to be repeated. Self interaction gives the opportunity to build new stochastic search algorithms with the ability to, in a sense, re-initialize their exploration from the past, re-starting from some better fitted previously visited initial value. In this context, we plan to explore the theoretical foundations and the numerical analysis of continuous time or discrete generation self-organized systems by combining spatial and temporal mean field interaction mechanisms.

The last generation of stochastic random tree models is concerned with biology-inspired algorithms on paths and excursions spaces. These genealogical adaptive search algorithms coincide with genetic type particle models in excursion spaces. They have been successfully applied in generating the excursion distributions of Markov processes evolving in critical and rare event regimes, as well as in path estimation and related smoothing problems arising in advanced signal processing. The complete mathematical analysis of these random tree models, including their long time behavior, their propagation of chaos properties, as well as their combinatorial structures are far from complete.

Our research agenda on stochastic learning is developed around the applied mathematical axis as well as the numerical perspective, including concrete industrial transfers with a special focus on Naval Group. From the theoretical side, mid-term objectives are centered around non asymptotic performance analysis and long time behavior of Monte Carlo methods and stochastic learning algorithms. We also plan to further develop the links with Bayesian statistical learning methodologies and artificial intelligence techniques, including the analysis of genetic programming discussed in section . We also have several long term projects. The first one is to develop new particle type methodologies to solve high dimensional data assimilation problems arising in forecasting and fluid mechanics, as well as in statistical machine learning. We also plan to design stochastic filtering-type algorithms to solve partially observed control problems such as those discussed in section .

In the last three decades, the use of Feynman-Kac type particle models has been developed in variety of scientific disciplines, including in molecular chemistry, risk analysis, biology, signal processing, Bayesian inference and data assimilation.

The design and the mathematical analysis of Feynman-Kac particle methodologies has been one of the main research topics of P. Del Moral since the late 1990's , , , see also the books , , , and references therein. These mean field particle sampling techniques encapsulate particle filters, sequential Monte Carlo methods, spatial branching and evolutionary algorithms, Fleming-Viot genetic type particles methods arising in the computation of quasi-invariant measures and simulation of non absorbed processes, as well as diffusion Monte Carlo methods arising in numerical physics and molecular chemistry. The term "particle filters" was first coined in the article published in 1996 in reference to branching and mean field interacting particle methods used in fluid mechanics since the beginning of the 1960s. This article presents the first rigorous analysis of these mean field type particle algorithms.

The INRIA project teams applying the particle methodology developed by ASTRAL include the INRIA project team SIMSMART (rare event simulation as well as particle filters) and the INRIA project team Matherials (applications in molecular chemistry). The project team ASTRAL also has several collaborative research projects with these, teams as well as with researchers from international universities working in this subject, including Oxford, Cambridge, New South Wales Sydney, UTS, Bath, Warwick and Singapore Universities.

Permanent researchers: P. Del Moral, F. Dufour, A. Genadot, E. Horton, D. Laneuville, O. Marceau, A. Nègre, J. Saracco, H. Zhang.

Part of this research project is devoted to the analysis of stochastic decision models. Many real applications in dynamic optimization can be, roughly speaking, described in the following way: a certain system evolves randomly under the control of a sequence of actions with the objective to optimize a performance function. Stochastic decision processes have been introduced in the literature to model such situations and it is undoubtedly their generic capacity to model real life applications that leads to and continues to contribute to their success in many fields such as engineering, medicine and finance.

In this project we will focus on specific families of models that can be identified according to the following elements: the nature of the time variable (discrete or continuous), the type of dynamics (piecewise deterministic trajectories) and the numbers of decision makers. For one player, the system will be called a stochastic control process and for the case of several decision-makers, the name (stochastic) game will be used. For ease of understanding, we now provide an informal description of the classes of stochastic processes we are interested in, according to the nature of the time variable.

In this framework, the basic model can be described by a state space where the system evolves, an action space, a stochastic kernel governing the dynamic and, depending on the state and action variables, a one-step cost (reward) function. The distribution of the controlled stochastic process is defined through the control policy which is then selected in order to optimize the objective function. This is a very general model for dynamic optimization in discrete-time, which also goes by the name of stochastic dynamic programming.
For references, the interested reader may consult the following books , , , , , , , , , , and the references therein (this list of references is, of course, not exhaustive).

Most of the continuous-time stochastic processes consist of a combination of the following three different ingredients: stochastic jumps, diffusion and deterministic motions. In this project, we will focus on non-diffusive models, in other words, stochastic models for which the randomness appears only at fixed or random times, i.e. those combining deterministic motions and random jumps.
These stochastic processes are the so-called piecewise deterministic Markov processes (PDMPs) , , , , , , . This family of models plays a central role in applied probability because it forms the bulk of models in many research fields such as, e.g. operational research, management science and economy and covers an enormous variety of applications.

These models can be framed in several different forms of generality, depending on their mathematical properties such as the type of performance criterion, full or incomplete state information, with or without constraints, adaptative or not, but more importantly, the nature of the boundary of the state space, the type of dynamic between two jumps and on the number of decision-makers. These last three characteristics make the analysis of the controlled process much more involved.

Part of this project will cover both theoretical and numerical aspects of stochastic optimal control.
It is clear that stochastic problems and control games have been extensively studied in the literature.
Nevertheless, important challenges remain to be addressed.
From the theoretical side, there are still many technical issues that are, for the moment, still unanswered or at most have received partial answers.
This is precisely what makes them difficult and requires either the creative transposition of pre-existing methodologies or the development of new approaches.
It is interesting to note that one of the feature of these theoretical problems is that they are closely related to practical issues. Solving such problems not only gives rise to challenging mathematical questions, but also allow a better insight into the structure and properties of real practical problems.
Theory for applications will be for us the thrust that will guide us in this project.
From the numerical perspective, solving a stochastic decision model remains a critical issue.
Indeed, except for very few specific models, the determination of an optimal policy and the associated value function is an extremely difficult problem to tackle.
The development of computational and numerical methods to get quasi-optimal solutions is, therefore, of crucial importance to demonstrate the practical interest of stochastic decision model as a powerful modeling tool.
During the International Conference on Dynamic Programming and Its Applications held at the University of British Columbia, Canada in April 1977, Karl Hinderer, a pioneer in the field of stochastic dynamic programming emphasized that "whether or not our field will have a lasting impact on science beyond academic circles depends heavily on the success of implemented applications". We believe that this statement is still in force some forty years later.

The objective of this project is to address these important challenges. They are mainly related to models with general state/action spaces and with continuous time variables covering a large field of applications. Here is a list of topics we would like to study: games, constrained control problems, non additive types of criteria, numerical and computational challenges, analysis of partially observed/known stochastic decision processes. This list is not necessarily exhaustive and may of course evolve over time.

Our research agenda on optimal stochastic control is developed around the applied mathematical axis as well as the numerical perspective, including concrete industrial transfers with a special focus on Naval Group. Our mid-term objectives will focus on the following themes described above: properties of control policies in continuous-time control problems, non additive types of criteria, numerical and computational challenges. Our long-term objectives will focus on the analysis of partially observed/known stochastic control problems, constrained control problems and games.

There exists a large national/international community working on PDMPs and MDPs both on the theoretical, numerical and practical aspects. One may cite A. Almudevar (University of Rochester, USA), E. Altman (INRIA Team NEO, France), K. Avrachenkov (INRIA Team NEO, France), N. Bauerle (Karlsruhe University, Germany), D. Bertsekas (Massachusetts Institute of Technology, USA), O. Costa (Sao Paulo University, Brazil), M. Davis (Imperial College London, England), E. Feinberg (Stony Brook University, USA), D. Goreac (Université Paris-Est Marne-la-Vallée, France), X. Guo (Zhongshan University, China), O. Hernandez-Lerma (National Polytechnic Institute, Mexico), S. Marcus (University of Maryland, USA), T. Prieto-Rumeau (Facultad de Ciencias, UNED, Spain), A. Piunovskiy (University of Liverpool, England), U. Rieder (Universität Ulm, Germany), J. Tsitsiklis (Massachusetts Institute of Technology, USA), B. Van Roy (Stanford University, USA), O. Vega-Amaya (Universidad de Sonora, Mexico), Y. Zhang (University of Liverpool, England) to name just a few. Many of the colleagues cited above are at the head of research groups which have not been described in detail due to space limitation and so, this list is far from being exhaustive.

To some extent, our team is in competition wit the colleagues and teams mentioned above. We emphasize that there exists a long standing collaboration between our group and O. Costa (Sao Paulo University, Brazil) since 1998. In the last 10 years, we have established very fruitful collaborations with T. Prieto-Rumeau (Facultad de Ciencias, UNED, Spain) and A. Piunovskiy (University of Liverpool, England).

Inside INRIA, the team NEO and in particular E. Altman and K. Avrachenkov work on discrete-time MDPs but they are mainly focused on the case of countable (finite) state/action spaces MDPs. From this point of view, our results on this theme may appear complementary to theirs.

It is important to point out that (for the time being) only a sub-group of the academic part of the team collaborates with Naval Group. Initially the topics of interest for Naval Group was focused on filtering and control problems. The academic members of this sub-group are P. Del Moral, F. Dufour, A. Genadot, E. Horton and H. Zhang. It is also important to emphasize that Naval Group is undoubtedly our privileged industrial partner. This collaboration is described in section . For reasons of confidentiality, this section is not very detailed, in particular it does not mention the timetable and does not detail the technical solutions that will be considered. Our aim in the short term is to integrate the remaining academic team members into the group to work on the themes of interest to NG. A seminar was organized for this purpose in August 2020. The academic members of the team who are not involved in collaboration with NG (M. Chavent, P. Legrand and J. Saracco) have their own industrial collaborations that are described in section .

Permanent researchers: P. Del Moral, F. Dufour, A. Genadot, E. Horton, D. Laneuville, O. Marceau, A. Nègre and H. Zhang.

An important line of research of the team is submarine passive target tracking. This is a very complicated practical problem that combines both filtering and stochastic control topics. In the context of passive underwater acoustic warfare, let us consider a submarine, called the observer, equipped with passive sonars collecting noisy bearing-only measurements of the target(s). The trajectory of the observer has to be controlled in order to satisfy some given mission objectives. These can be, for example, finding the best trajectory to optimize the state estimation (position and velocity) of the targets, maximize the different targets' detection range and/or minimize its own acoustic indiscretion with respect to these targets, and reaching a way-point without being detected. Let us now describe in more detail some of the topics we intend to work on.

In the case of passive tracking problems, one of the main issues is that the observer must manoeuvre in order to generate observability. It turns out that these manoeuvres are actually necessary but not sufficient to guarantee that the problem becomes observable. In fact, a significant body of the literature pertains to attempting to understand whether this type of problem is solvable. Despite this observability analysis, the following practical questions, which we would like to address in this project, remain challenging: What kind of trajectory should the observer follow to optimize the estimation of the target’s motion? What is the accuracy of that estimate? How to deal with a multitarget environment? How to take into account some physical constraints related to the sonar?

Another aspect of target tracking is to take into account both the uncertainties on the target's measurement and also the signal attenuation due to acoustic propagation. To the best of our knowledge, there are few works focusing on the computation of optimal trajectories of underwater vehicles based on signal attenuation. In this context, we would like address the problem of optimizing the trajectory of the observer to maximize the detection of the acoustic signals issued by the targets. Conversely, given that the targets are also equipped with sonars, how can one optimize the trajectory of the observer itself to keep its own acoustic indiscretion as low as possible with respect to those targets.

It must be emphasized that a human operator can find a suitable trajectory for either of these objectives in the context of a single target. However, if both criteria and/or several targets are taken into account simultaneously, it is hardly possible for a human operator to find such trajectories. From an operational point of view, these questions are therefore of great importance.

Such practical problems are strongly connected to the mathematical topics described in sections and . For example it is clearly related to partially observed stochastic control problems. The algorithmic solutions that we will develop in the framework of submarine passive target tracking will be evaluated on the basis of case studies proposed by Naval Group. Our short-term aim is to obtain explicit results and to develop efficient algorithms to solve the various problems described above.

Permanent researchers: M. Chavent, P. Legrand and J. Saracco.

For several years, the team has also had strong collaborations with INRAE which is the French National Research Institute for Agriculture, Food and Environment. More precisely, consumer satisfaction when eating beef is a complex response based on subjective and emotional assessments. Safety and health are very important in addition to taste and convenience but many other parameters are also extremely important for breeders. Many models were recently developed in order to predict each quality trait and to evaluate the possible trade-off that could be accepted in order to satisfy all the operators of the beef chain at the same time. However, in none of these quality prediction systems are issues of joint management of the different expectations addressed. Thus, it is vital to develop a model that integrates the sensory quality of meat but also its nutritional and environmental quality, which are expectations clearly expressed by consumers. Our team are currently developing statistical models and machine learning tools in order to simultaneously manage and optimize the different sets of expectations. Combining dimension reduction methodologies, nonparametric quantiles estimation and “Pareto front’’ approaches could provide an interesting way to address this complex problem. These different aspects are currently in progress.

The team is currently initiating scientific collaboration with the Advanced Data Analytics Group of Sartorius Corporate Research which is an international pharmaceutical and laboratory equipment supplier, covering the segments of Bioprocess Solutions and Lab Products & Services.
The current work concerns the development of a partial least squares (PLS) inspired method in the context of multiblocks of covariates (corresponding to different technologies and/or different sampling techniques and statistical procedures) and high dimensional datasets (with the sample size ddsPLS + Koh Lanta methodology is computationally fast. Some technical and/or theoretical work on this methodology must be naturally pursued in order to further refine this approach.
Moreover, another aspect of the future research with Sartorius consists of associating the structures of datasets with the
real biological dynamics described, until now, by differential equations and for which the most
advanced solutions do not merge with both high dimensional multiblock analysis and these
differential equations. Combining these two approaches in a unified framework will certainly
have many applications in industry and especially in the biopharmaceutical production.

Within the framework of the GIS ALBATROS, the team has initiated a scientific collaboration with IMS and THALES. The first topic is focused on the measurement of the cognitive load of a pilot through the development of methods for measuring the regularity of biological signals (Hölderian regularity, Detrended Fluctuation Analysis, etc.). The second topic is dedicated to the development of classification techniques of vessels. The different methods we proposed are based on deep learning, evolutionary algorithms and signal processing techniques and are compared to the approaches in the literature.

A total of 436 young cattle from 15 cattle breeds were reared in as similar conditions as possible to evaluate the impact of breed on sensory quality of beef from longissimus muscle determined by sensory analysis. In , two statistical methods for processing the sensory data were compared. The analysis of variance with or without the panelist effect gave similar conclusions indicating that the robustness of the results was not dependent on the method chosen. The 4 meat descriptors (tenderness, juiciness, beef flavor and off-flavor) placed breeds into 5 groups using an unsupervised classification (hierarchical ascending classification). Aberdeen Angus, Highland and Jersey, that have a high lipid content in the muscle studied, differed from the other breeds in that they had a higher beef flavour. The dual-purpose and rustic breeds, Simmental, Casina and Marchigiana, produced significantly less juicy and less tender meat than that from breeds selected for meat production. Overall, despite significant differences previously identified for animal, carcass, muscle and beef traits for the same animals, differences in sensory scores between most of the breeds were small, with only significant differences between the few breeds that had extreme sensory profiles (such as Simmental and Pirenaica).

Authors: A. Conanec; M. D. M. Campo; I. Richardson; P. Ertbjerg; S. Failla, B. Panea; M. Chavent (ASTRAL); J. Saracco (ASTRAL); J. L. Williams; M.-P. Ellies-Oury and J.-F. Hocquette.

In the beef sector, one of the major challenges is to early predict carcass and meat quality and to jointly satisfy the multiple expectations of the various stakeholders. Thus, the objective of this study was to determine if the relationships among carcass, nutritional and sensory qualities established previously by Ellies-Oury et al. (2016) might be generalized to different type of animals.

The Longissimus thoracis muscles of 32 young Charolais bulls were analyzed in terms of sensory and nutritional quality (lipid content and fatty acid composition). These parameters of interest were linked together and to animal performances by using a clustering of variables.

Longissimus thoracis sensory and nutritional qualities appear sometimes antagonistic. Indeed, some “positive” sensory descriptors (juiciness, overall appreciation, overall flavor and overall odor) are negatively related to PUFA proportions. PUFA proportions are positively associated with carcass weight but in the same time with rancid/fish flavors. Moreover, CLA and trans MUFA proportions are positively associated with the “negative” descriptors of greasy feel and residues. To finish, carcass weight and ADG are negatively associated with some “positive” sensory descriptors except tenderness.

It can be concluded from this work that these relationships, that were already established in previous works, are robust between experiments.

In order to highlight robust and generalizable relationships in different contexts, it is now appropriate to apply this method to a larger database containing different traits and various characteristics of breed, slaughter ages, animal types, fattening practices.

Authors: M.-P. Ellies-Oury; D. Durand; A, Listrat; M. Chavent (ASTRAL); J. Saracco (ASTRAL) and D. Gruffat.

Authors: F. Dufour (ASTRAL) and O. Costa.

When solving optimal impulse control problems, one can use the dynamic programming approach in two different ways: at each time moment, one can make the decision whether to apply a particular type of impulse, leading to the instantaneous change of the state, or apply no impulses at all; or, otherwise, one can plan an impulse after a certain interval, so that the length of that interval is to be optimised along with the type of that impulse. The first method leads to the differential Bellman equation, while the second method leads to the integral Bellman equation. The target of the current article is to prove the equivalence of those Bellman equations in many specific models. Those include abstract dynamical systems, controlled ordinary differential equations, piece-wise deterministic Markov processes and continuous-time Markov decision processes.

Authors: F. Dufour (ASTRAL); A. Piunovskiy and A. Plakhov.

Authors: F. Dufour (ASTRAL) and T. Prieto-Rumeau

Authors: F. Dufour (ASTRAL) and T. Prieto-Rumeau

Since its introduction in the early 90s, the Sliced Inverse Regression (SIR) methodology has evolved adapting to increasingly complex data sets in contexts combining linear dimension reduction with non linear regression. The assumption of dependence of the response variable with respect to only a few linear combinations of the covariates makes it appealing for many computational and real data application aspects. This work proposes an overview of the most active research directions in SIR modeling from multivariate regression models to regularization and variable selection.

Authors: S. Girard; H. Lorenzo (ASTRAL) and J. Saracco (ASTRAL).

Sliced inverse regression (SIR) focuses on the relationship between a dependent variable

Authors: H. Lorenzo (ASTRAL) and J. Saracco (ASTRAL).

In the supervised high dimensional settings with a large number of variables and a low number of individuals, variable selection allows a simpler interpretation and more reliable predictions. That subspace selection is often managed with supervised tools when the real question is motivated by variable prediction. In we propose a Partial Least Square (PLS) based method, called data-driven sparse PLS (ddsPLS), allowing variable selection both in the covariate and the response parts using a single hyper-parameter per component. The subspace estimation is also performed by tuning a number of underlying parameters. The ddsPLS method is compared to existing methods such as classical PLS and two well established sparse PLS methods through numerical simulations. The observed results are promising both in terms of variable selection and prediction performance. This methodology is based on new prediction quality descriptors associated with the classical R 2 and Q 2 and uses bootstrap sampling to tune parameters and select an optimal regression model.

Authors: H. Lorenzo (ASTRAL); O. Cloarec; R. Thiébaut and J. Saracco (ASTRAL).

Authors: M. Chavent (ASTRAL); J. Lacaille; A. Mourer and M. Olteanu.

Discrete algebraic Riccati equations and their fixed points are well understood and arise in a variety of applications, however, the time-varying equations have not yet been fully explored in the literature. In this article we provide a self-contained study of discrete time Riccati matrix difference equations. In particular, we provide a novel Riccati semigroup duality formula and a new Floquet-type representation for these equations. Due to the aperiodicity of the underlying flow of the solution matrix, conventional Floquet theory does not apply in this setting and thus further analysis is required. We illustrate the impact of these formulae with an explicit description of the solution of time-varying Riccati difference equations and its fundamental-type solution in terms of the fixed point of the equation and an invertible linear matrix map, as well as uniform upper and lower bounds on the Riccati maps. These are the first results of this type for time varying Riccati matrix difference equations.

Authors: P. Del Moral (ASTRAL) and E. Horton (ASTRAL).

Authors: P. Del Moral (ASTRAL); E. Horton (ASTRAL) and A. Jasra.

Authors: P. Del Moral (ASTRAL) and E. Horton (ASTRAL).

Suppose that

Authors: I. Gonzalez; E. Horton (ASTRAL) and A. E. Kyprianou.

Growth-fragmentation processes model systems of cells that grow continuously over time and then fragment into smaller pieces. Typically, on average, the number of cells in the system exhibits asynchronous exponential growth and, upon compensating for this, the distribution of cell sizes converges to an asymptotic profile. However, the long-term stochastic behaviour of the system is more delicate, and its almost sure asymptotics have been so far largely unexplored. In this article , we study a growth-fragmentation process whose cell sizes are bounded above, and prove the existence of regimes with differing almost sure long-term behaviour.

Authors: E. Horton (ASTRAL) and A. R. Watson.

The neutron transport equation (NTE) describes the flux of neutrons over time through an inhomogeneous fissile medium. In the recent articles, [A. M. G. Cox et al., J. Stat. Phys., 176 (2019), pp. 425–455; E. Horton, A. E. Kyprianou, and D. Villemonais, Ann. Appl. Probab., 30 (2020), pp. 2573–2612] a probabilistic solution of the NTE is considered in order to demonstrate a Perron–Frobenius type growth of the solution via its projection onto an associated leading eigenfunction. In [S. C. Harris, E. Horton, and A. E. Kyprianou, Ann. Appl. Probab., 30 (2020), pp. 2815–2845; A. M. G. Cox et al., Monte Carlo Methods for the Neutron Transport Equation, (2020)], further analysis is performed to understand the implications of this growth both in the stochastic sense as well as from the perspective of Monte Carlo simulation. Such Monte Carlo simulations are prevalent in industrial applications, in particular where regulatory checks are needed in the process of reactor core design. In that setting, however, it turns out that a different notion of growth takes center stage, which is otherwise characterized by another eigenvalue problem. In that setting, the eigenvalue, sometimes called

Authors: A. M. G. Cox; E. Horton (ASTRAL); A. E. Kyprianou and D. Villemonais

Authors: S. C. Harris; E. Horton (ASTRAL); A. E. Kyprianou and M. Wang.

Despite the widespread usage of discrete generation Ensemble Kalman particle filtering methodology to solve nonlinear and high dimensional filtering and inverse problems, little is known about their mathematical foundations. As genetic-type particle filters (a.k.a. sequential Monte Carlo), this ensemble-type methodology can also be interpreted as mean-field particle approximations of the Kalman-Bucy filtering equation. In contrast with conventional mean-field type interacting particle methods equipped with a globally Lipschitz interacting drift-type function, Ensemble Kalman filters depend on a nonlinear and quadratic-type interaction function defined in terms of the sample covariance of the particles. Most of the literature in applied mathematics and computer science on these sophisticated interacting particle methods amounts to designing different classes of useable observer-type particle methods. These methods are based on a variety of inconsistent but judicious ensemble auxiliary transformations or include additional inflation/localisationtype algorithmic innovations, in order to avoid the inherent time-degeneracy of an insufficient particle ensemble size when solving a filtering problem with an unstable signal. To the best of our knowledge, the first and the only rigorous mathematical analysis of these sophisticated discrete generation particle filters is developed in the pioneering articles by Le Gland-Monbet-Tran and by Mandel-Cobb-Beezley, which were published in the early 2010s. Nevertheless, besides the fact that these studies prove the asymptotic consistency of the Ensemble Kalman filter, they provide exceedingly pessimistic meanerror estimates that grow exponentially fast with respect to the time horizon, even for linear Gaussian filtering problems with stable one dimensional signals. In the present article we develop a novel self-contained and complete stochastic perturbation analysis of the fluctuations, the stability, and the long-time performance of these discrete generation ensemble Kalman particle filters, including time-uniform and non-asymptotic mean-error estimates that apply to possibly unstable signals. To the best of our knowledge, these are the first results of this type in the literature on discrete generation particle filters, including the class of genetic-type particle filters and discrete generation ensemble Kalman filters. The stochastic Riccati difference equations considered in this work are also of interest in their own right, as a prototype of a new class of stochastic rational difference equation.

Authors: P. Del Moral (ASTRAL) and E. Horton (ASTRAL)

Terrain Aided Navigation (TAN) provides a driftfree navigation approach for Unmanned Underwater Vehicles. This paper focuses on an improved version of the Weighted Ensemble Kalman Filter (WEnKF) to solve the TAN problem. We analyze some theoretical limitations of the WEnKF and derive an improved version which ensures that the asymptotic variance of weights remains bounded. This improvement results in an enhanced robustness to nonlinearities in practice. Numerical results are presented and the robustness is demonstrated with respect to conventional WEnKF, yielding twice as less nonconvergence cases.

Authors: C. Palmier (ASTRAL); K. Dahia; N. Merlinge; D. Laneuville (ASTRAL) and P. Del Moral (ASTRAL).

The detrended fluctuation analysis (DFA) and its variants are popular methods to analyze the self-similarity of a signal. Two steps characterize them: firstly, the trend of the centered integrated signal is estimated and removed. Secondly, the properties of the so-called fluctuation function which is an approximation of the standard deviation of the resulting process is analyzed. However, it appears that the statistical mean was assumed to be equal to zero to obtain it. As there is no guarantee that this assumption is true a priori, this hypothesis is debatable. The purpose of this paper is to propose two alternative definitions of the fluctuation function. Then, we compare all of them based on a matrix formulation and the filter-based interpretation we recently proposed. This analysis will be useful to show that the approach proposed in the original paper remains a good compromise in terms of accuracy and computational cost.

Authors: B. Berthelot; E. Grivel; P. Legrand (ASTRAL) and A. Giremus.

The fluctuation analysis (FA) and the detrended fluctuation analysis (DFA) make it possible to estimate the Hurst exponent H, which characterizes the self-similarity of a signal. Both are based on the fact that the so-called fluctuation function, which can be seen as an approximation of the standard deviation of the process scaled in time by multiplying the time variable by a positive constant, depends on H. The main novelty of the paper is to provide the expression of the variance of the square of the fluctuation function, by using a matrix formulation. We show that it depends on the correlation function of the signal under study when it is zero-mean and Gaussian. Illustrations are given when dealing with a zero-mean white Gaussian noise. Moving average processes and first-order autoregressive processes are also addressed.

Authors: E. Grivel; B. Berthelot and P. Legrand (ASTRAL).

The detrended fluctuation analysis (DFA) and its higher-order variant make it possible to estimate the Hurst exponent and therefore to quantify the long-range dependence of a random process. These methods are popular and used in a wide range of applications where they have been proven to be discriminative to characterize or classify processes. Nevertheless, in practice, the signal may be short-memory. In addition, depending on the number of samples available, there is no guarantee that these methods provide the true value of the Hurst exponent, leading the user to draw erroneous conclusions on the long-range dependence of the signal under study. In this paper , using a matrix formulation and making no approximation, we first propose to analyze how the DFA and its higher-order variant behave with respect to the number of samples available. Illustrations dealing with short-memory data that can be modeled by a white noise, a moving-average process and a random process whose autocorrelation function exponentially decays are given. Finally, to avoid any wrong conclusions, we propose to derive abacuses linking the value provided by the DFA or its variant with the properties of the signal and the number of samples available.

Authors: E. Grivel; B. Berthelot; P. Legrand (ASTRAL) and A. Giremus

Proposed early in the 90ies, the detrended fluctuation analysis (DFA) can be used to estimate the Hurst exponent and has been proven relevant in various applications, from economics to biomedical. For the last years, variants have been proposed. They differ in the way to estimate the trend of the centered integrated signal. In this paper , we recall the main principles of some of these methods, provide explanations on the behaviours of the algorithms and analyze the relevance of new variants based on the Savitzky-Golay filter, also known as the LOESS approach, and the LOWESS. They bridge the gap between the DFA and the detrending moving average (DMA). We hence show that the LOESS-based method is a generalization of the DMA.

Authors: B. Berthelot; E. Grivel and P. Legrand (ASTRAL).

An astonishing team of researchers, teachers and, above all, lovers of the Langue d'Oc with diverse and varied skills, has been carrying out a systematic analysis of the Bourciez survey for just over a year. In 1894, Edouard Bourciez, then a professor at the University of Bordeaux, asked teachers in the Bordeaux and Toulouse academies to translate a reworked version of the parable of the prodigal son into the idiom of the commune where they taught. The results of this investigation undoubtedly exceeded the professor's expectations, since more than 4400 parables were returned to him. These manuscripts are now kept at the University of Bordeaux and form a corpus of 17 volumes of about 1000 manuscript pages each. We are very grateful to the university library for allowing us to have them. For reasons that are not easy to explain, a systematic analysis of this survey has never been carried out, except for the Basque part corresponding to 150 municipalities, i.e. less than 4 percent of the corpus. We are proceeding with the analysis of this survey according to the following four points, not in series, but in parallel: - Computer transcription of the manuscripts ; - Creation of the database; - Data mining and statistical analysis of the database; - Formatting of the results in a format accessible to the greatest number of people.

Author: Alexandre Genadot (ASTRAL)

The spatial organisation of the genome within the cell nucleus has a major impact on the regulation of gene expression, with important implications for foetal development, cell differentiation and disease development. This is the initial motivation for this work , which aims to study the three-dimensional structure of genetic material and its variations using Hi-C data. First, the modelling of the hierarchical structure of the genome from Hi-C data is investigated. Extensions of a natural statistical tool for examining hierarchical structures, Hierarchical Ascending Classification (HAC), are studied to justify its application to Hi-C. This allows us to justify the modelling of structures by binary trees (derived from HAC). We then develop a method for comparing two samples of trees to be able to identify significant differences.

Authors: N. Randriamihamison (ASTRAL).

Participants: P. Del Moral, F. Dufour, A. Genadot, E. Horton, D. Laneuville, O. Marceau, R. Namyst, A. Nègre, H. Zhang, R. Zhang.

In the applicative domain, an important research focus of the team is the tracking of passive underwater targets in the context of passive underwater acoustic warfare. This is a very complicated practical problem that combines both filtering and stochastic control issues. This research topic is addressed in collaboration with Naval Group. We refer the reader to the section for a more detailed description of this theme.

Participants: Benoîte de Saporta, François Dufour, Tiffany Cerchi.

Maintenance, optimization, fleet of industrial equipements. The topic of this collaboration with Université de Montpellier and Thales Optronique is the application of Markov decision processes to the maintenance optimization of a fleet of industrial equipments.

Participants: Bastien Berthelot, Pierrick Legrand.

The collaboration is centered around some contributions to the estimation of the Hurst coefficient and his application on biosignals in the domain of crew monitoring.

Participant: Pierrick Legrand.

Pierrick Legrand is a consultant for the startup Case Law Analytics. The object of the consulting is confidential.

Participants: Hadrien Lorenzo Jérôme Saracco.

The team is currently initiating a scientific collaboration with the Advanced Data Analytics Group of Sartorius Corporate Research which is an international pharmaceutical and laboratory equipment supplier, covering the segments of Bioprocess Solutions and Lab Products and Services. The current work concerns the development of a PLS (Partial Least Squares) inspired method in the context of multiblock of covariates (corresponding to different technologies and/or different sampling, statistical natures...) and high dimensional datasets (with the sample size n much smaller than the number of variables in the different blocks). The proposed method, called ddsPLS for data-driven sparse PLS, allows variable selection in the X and in the Y parts thanks to interpretable parameters associate with the soft-thresolding of the empirical correlation matrices (between the X's blocks and the Y block) decomposed in SVD (Singular Values Decomposition) ways. In addition a methodology to handle specific missing values (i.e. missing samples in some covariate blocks) is also under investigation.

Participants: Marie Chavent, Jérôme Lacaille, Alex Mourer, Madalina Olteanou

The collaboration is centered around an applied mathematics thesis defining a formalism and a methodology for processing and interpretation by the importance of variables (from measurements and calculated indicators) in the case of unsupervised problems. This methodology is accompanied by code programming and a demonstration on an example data set from Safran Aircraft Engines.

Participants: Tara Vanhatalo, Pierrick Legrand

Within the framework of Tara Vanhatalo's cifre thesis, a collaboration contract has been signed between Inria, Univiersité de Bordeaux and the company Orosys.

The collaboration is oriented around the modeling of amplifiers by AI.

Emma Horton and her collaborator Ellen Powell (University of Durham, UK) have been awarded a PHC Alliance 2022 grant.

Astral is a joint INRIA team project with Naval Group. The topic of this collaboration is described in section .

The mathematical analysis of metastable processes started 75 years ago with the seminal works of Kramers on Fokker-Planck equation. Although the original motivation of Kramers was to « elucidate some points in the theory of the velocity of chemical reactions », it turns out that Kramers’ law is observed to hold in many scientific fields: molecular biology (molecular dynamics), economics (modelization of financial bubbles), climate modeling, etc. Moreover, several widely used efficient numerical methods are justified by the mathematical description of this phenomenon.

Recently, the theory has witnessed some spectacular progress thanks to the insight of new tools coming from Spectral and Partial Differential Equations theory.

Semiclassical methods together with spectral analysis of Witten Laplacian gave very precise results on reversible processes. From a theoretical point of view, the semiclassical approach allowed to prove a complete asymptotic expansion of the small eigen values of Witten Laplacian in various situations (global problems, boundary problems, degenerate diffusions, etc.). The interest in the analysis of boundary problems was rejuvenated by recent works establishing links between the Dirichlet problem on a bounded domain and the analysis of exit event of the domain. These results open numerous perspectives of applications. Recent progress also occurred on the analysis of irreversible processes (e.g. on overdamped Langevin equation in irreversible context or full (inertial) Langevin equation).

The above progresses pave the way for several research tracks motivating our project: overdamped Langevin equations in degenerate situations, general boundary problems in reversible and irreversible case, non-local problems, etc.

The aim of MISGIVING (MathematIcal Secrets penGuins dIVING) is to use mathematical models to understand the complexity of the multiscale decision process conditioning not only the optimal duration of a dive but also the diving behaviour of a penguin inside a bout. A bout is a sequence of succesive dives where the penguin is chasing prey. The interplay between the chasing period (dives) and the resting period due to the physiological cost of a dive (the time spent at the surface) requires some kind of optimization.

F. Dufour has been invited to give a talk at the workshop Modern Trends in Controlled Stochastic Processes: Theory and Applications, University of Liverpool, July 2021.

F. Dufour has been invited to give a talk at Symposium on Stochastic Hybrid Systems and Applications, July 2021.

E. Horton was invited to give a mini-course at the 6th Bath-Paris-Beijing Branching Structures Workshop, Septemper 2021.

Pierrick Legrand is the president of the association .

J. Saracco is an elected member of the CNU 26 (National Council of Universities in Applied Mathematics), since 2019.

Emma Horton is a member of the Applied Probability committee.

J. Saracco is the leader of the team OptimAl of Institut de Mathématiques de Bordeaux (IMB, UMR CNRS 5251) from 2019.

Marie Chavent is a member appointed to the Council of the Department of Engineering and Digital Sciences (SIN) of the University of Bordeaux.

A. Genadot is member of the Scientific council of the Mathematical Institute of Bordeaux.

A. Genadot is member of the "Commission des emplois de recherche" of Inria BSO.

P. Legrand is member of the CUMI commission.

Pierrick Legrand was member of the PhD jurys of Ulviya Abdulkarimova, Nicolas Scalzitti and Pierre-antoine Chantal.