Keywords
 A6. Modeling, simulation and control
 A6.1. Methods in mathematical modeling
 A6.1.1. Continuous Modeling (PDE, ODE)
 A6.1.2. Stochastic Modeling
 A6.1.4. Multiscale modeling
 A6.2. Scientific computing, Numerical Analysis & Optimization
 A6.2.1. Numerical analysis of PDE and ODE
 A6.2.2. Numerical probability
 A6.2.3. Probabilistic methods
 A6.2.4. Statistical methods
 A6.2.5. Numerical Linear Algebra
 A6.2.6. Optimization
 A6.3. Computationdata interaction
 A6.3.1. Inverse problems
 A6.3.2. Data assimilation
 A6.3.4. Model reduction
 A6.3.5. Uncertainty Quantification
 A6.5. Mathematical modeling for physical sciences
 A6.5.2. Fluid mechanics
 A6.5.3. Transport
 A6.5.5. Chemistry
 B1. Life sciences
 B2. Health
 B3. Environment and planet
 B3.2. Climate and meteorology
 B4. Energy
 B4.2. Nuclear Energy Production
 B4.2.1. Fission
 B5.3. Nanotechnology
 B5.5. Materials
1 Team members, visitors, external collaborators
Research Scientists
 Mathias Rousset [Team leader, Inria, Researcher, HDR]
 Frédéric Cérou [Inria, Researcher]
 Cédric Herzet [Inria, Researcher]
 Patrick Héas [Inria, Researcher]
 François Le Gland [Inria, Senior Researcher]
Faculty Member
 Valérie Monbet [Univ de Rennes I, Professor, HDR]
PhD Students
 Francois Ernoult [Univ de Rennes I]
 Theo Guyard [INSA Rennes, from Oct 2021]
 Said Obakrim [Univ de Rennes I]
 Thu Le Tran [Univ de Rennes I]
Interns and Apprentices
 Jules Berry [Inria, from Jun 2021 until Jul 2021]
 Hala Bouzidi [Inria, from May 2021 until Jul 2021]
Administrative Assistants
 Fabienne Cuyollaa [Inria, until Jun 2021]
 Gunther Tessier [Inria, from Sep 2021]
2 Overall objectives
As the constant surge of computational power is nurturing scientists into simulating the most detailed features of reality, from complex molecular systems to climate or weather forecast, the computer simulation of physical systems is becoming reliant on highly complex stochastic dynamical models and very abundant observational data. The complexity of such models and of the associated observational data stems from intrinsic physical features, which do include high dimensionality as well as intricate temporal and spatial multiscales. It also results in much less control over simulation uncertainty.
Within this highly challenging context, SIMSMART positions itself as a mathematical and computational probability and statistics research team, dedicated to Monte Carlo simulation methods. Such methods include in particular particle Monte Carlo methods for rare event simulation, data assimilation and model reduction, with application to stochastic random dynamical physical models. The main objective of SIMSMART is to disrupt this now classical field by creating deeper mathematical frameworks adapted to the management of contemporary highly sophisticated physical models.
3 Research program
Introduction. Computer simulation of physical systems is becoming increasingly reliant on highly complex models, as the constant surge of computational power is nurturing scientists into simulating the most detailed features of reality – from complex molecular systems to climate/weather forecast.
Yet, when modeling physical reality, bottomup approaches are stumbling over intrinsic difficulties. First, the timescale separation between the fastest simulated microscopic features, and the macroscopic effective slow behavior becomes huge, implying that the fully detailed and direct long time simulation of many interesting systems (e.g. large molecular systems) are out of reasonable computational reach. Second, the chaotic dynamical behaviors of the systems at stake, coupled with such multiscale structures, exacerbate the intricate uncertainty of outcomes, which become highly dependent on intrinsic chaos, uncontrolled modeling, as well as numerical discretization. Finally, the massive increase of observational data addresses new challenges to classical data assimilation, such as dealing with high dimensional observations and/or extremely long time series of observations.
SIMSMART Identity. Within this highly challenging applicative context, SIMSMART positions itself as a computational probability and statistics research team, with a mathematical perspective. Our approach is based on the use of stochastic modeling of complex physical systems, and on the use of Monte Carlo simulation methods, with a strong emphasis on dynamical models. The two main numerical tasks of interest to SIMSMART are the following: (i) simulating with pseudorandom number generators  a.k.a. sampling  dynamical models of random physical systems, (ii) sampling such random physical dynamical models given some real observations  a.k.a. Bayesian data assimilation. SIMSMART aims at providing an appropriate mathematical level of abstraction and generalization to a wide variety of Monte Carlo simulation algorithms in order to propose nonsuperficial answers to both methodological and mathematical challenges. The issues to be resolved include computational complexity reduction, statistical variance reduction, and uncertainty quantification.
SIMSMART Objectives. The main objective of SIMSMART is to disrupt this now classical field of particle Monte Carlo simulation by creating deeper mathematical frameworks adapted to the challenging world of complex (e.g. high dimensional and/or multiscale), and massively observed systems, as described in the beginning of this introduction.
To be more specific, we will classify SIMSMART objectives using the following four intertwined topics:
 Objective 1: Rare events and random simulation.
 Objective 2: High dimensional and advanced particle filtering.
 Objective 3: Nonparametric approaches.
 Objective 4: Model reduction and sparsity.
Rare events Objective 1 are ubiquitous in random simulation, either to accelerate the occurrence of physically relevant random slow phenomenons, or to estimate the effect of uncertain variables. Objective 1 will be mainly concerned with particle methods where splitting is used to enforce the occurrence of rare events.
The problem of high dimensional observations, the main topic in Objective 2, is a known bottleneck in filtering, especially in nonlinear particle filtering, where linear data assimilation methods remain the stateoftheart approaches.
The increasing size of recorded observational data and the increasing complexity of models also suggest to devote more effort into nonparametric data assimilation methods, the main issue of Objective 3.
In some contexts, for instance when one wants to compare solutions of a complex (e.g. high dimensional) dynamical systems depending on uncertain parameters, the construction of relevant reducedorder models becomes a key topic. Model reduction aims at proposing efficient algorithmic procedures for the resolution (to some reasonable accuracy) of highdimensional systems of parametric equations. This overall objective entails many different subtasks:1) the identification of lowdimensional surrogates of the target “solution’’ manifold, 2) The devise of efficient methodologies of resolution exploiting lowdimensional surrogates, 3) The theoretical validation of the accuracy achievable by the proposed procedures. This is the content of Objective 4.
With respect to volume of research activity, Objective 1, Objective 4 and the sum (Objective 2+Objective 3) are comparable.
Some new challenges in the simulation and data assimilation of random physical dynamical systems have become prominent in the last decade. A first issue (i) consists in the intertwined problems of simulating on large, macroscopic random times, and simulating rare events (see Objective 1). The link between both aspects stems from the fact that many effective, large times dynamics can be approximated by sequences of rare events. A second, obvious, issue (ii) consists in managing very abundant observational data (see Objective 2 and 3). A third issue (iii) consists in quantifying uncertainty/sensitivity/variance of outcomes with respect to models or noise. A fourth issue (iv) consists in managing high dimensionality, either when dealing with complex prior physical models, or with very large data sets. The related increase of complexity also requires, as a fifth issue (v), the construction of reduced models to speedup comparative simulations (see Objective 4). In a context of very abundant data, this may be replaced by a sixth issue (vi) where complexity constraints on modeling is replaced by the use of nonparametric statistical inference (see Objective 3).
Hindsight suggests that all the latter challenges are related. Indeed, the contemporary digital condition, made of a massive increase in computational power and in available data, is resulting in a demand for more complex and uncertain models, for more extreme regimes, and for using inductive approaches relying on abundant data. In particular, uncertainty quantification (item (iii)) and high dimensionality (item (iv)) are in fact present in all 4 Objectives considered in SimSmart.
4 Application domains
4.1 Domain 1 – Computational Physics
The development of largescale computing facilities has enabled simulations of systems at the atomistic scale on a daily basis. The aim of these simulations is to bridge the time and space scales between the macroscopic properties of matter and the stochastic atomistic description. Typically, such simulations are based on the ordinary differential equations of classical mechanics supplemented with a random perturbation modeling temperature, or collisions between particles.
Let us give a few examples. In biochemistry, such simulations are key to predict the influence of a ligand on the behavior of a protein, with applications to drug design. The computer can thus be used as a numerical microscope in order to access data that would be very difficult and costly to obtain experimentally. In that case, a rare event (Objective 1) is given by a macroscopic system change such as a conformation change of the protein. In nuclear safety, such simulations are key to predict the transport of neutrons in nuclear plants, with application to assessing aging of concrete. In that case, a rare event is given by a high energy neutron impacting concrete containment structures.
A typical model used in molecular dynamics simulation of open systems at given temperature is a stochastic differential equation of Langevin type. The large time behavior of such systems is typically characterized by a hopping dynamics between 'metastable' configurations, usually defined by local minima of a potential energy. In order to bridge the time and space scales between the atomistic level and the macroscopic level, specific algorithms enforcing the realization of rare events have been developed. For instance, splitting particle methods (Objective 1) have become popular within the computational physics community only within the last few years, partially as a consequence of interactions between physicists and Inria mathematicians in ASPI (parent of SIMSMART) and MATHERIALS projectteams.
SIMSMART also focuses on various models described by partial differential equations (reactiondiffusion, conservation laws), with unknown parameters modeled by random variables.
4.2 Domain 2 – Meteorology
The traditional trend in data assimilation in geophysical sciences (climate, meteorology) is to use as prior information some very complex deterministic models formulated in terms of fluid dynamics and reflecting as much as possible the underlying physical phenomenon (see e.g.). Weather/climate forecasting can then be recast in terms of a Bayesian filtering problem (see Objective 2) using weather observations collected in situ.
The main issue is therefore to perform such Bayesian estimations with very expensive infinite dimensional prior models, and observations in large dimension. The use of some linear assumption in prior models (Kalman filtering) to filter nonlinear hydrodynamical phenomena is the stateoftheart approach, and a current field of research, but is plagued with intractable instabilities.
This context motivates two research trends: (i) the introduction of nonparametric, modelfree prior dynamics constructed from a large amount of past, recorded real weather data; and (ii) the development of appropriate nonlinear filtering approaches (Objective 2 and Objective 3).
SIMSMART will also test its new methods on multisource data collected in NorthAtlantic paying particular attention to coastal areas (e.g. within the interLabex SEACS).
4.3 Other Applicative Domains
SIMSMART focuses on various applications including:
 Tracking and hidden Markov models.
 Robustness and certification in Machine Learning.
5 Social and environmental responsibility
5.1 Footprint of research activities
Zero: no travel by plane; no intensive numerical computing.
6 New software and platforms
The following softwares have been released or updated.
6.1 New software
6.1.1 Screening4L0Problem

Keywords:
Global optimization, Sparsity

Functional Description:
This software contains "Branch and bound" optimization routines exploiting "screening" acceleration rules for solving sparse representation problems involving the L0 pseudonorm.
 URL:
 Publication:

Contact:
Cedric Herzet

Participants:
Clement Elvira, Theo Guyard
6.1.2 Screen&Relax

Keywords:
Optimization, Sparsity

Functional Description:
This software provides optimization routines to efficiently solve the "ElasticNet" problem.
 URL:
 Publication:

Contact:
Cedric Herzet

Participants:
Clement Elvira, Theo Guyard
6.1.3 npSEM

Name:
Stochastic expectationmaximization algorithm for nonparametric statespace models

Keyword:
Statistic analysis

Functional Description:
npSEM is the combination of a nonparametric estimate of the dynamic using local linear regression (LLR), a conditional particle smoother and a stochastic ExpectationMaximization (SEM) algorithm. Further details of its construction and implementation are introduced in the article An algorithm for nonparametric estimation in statespace models of authors "T.T.T. Chau, P. Ailliot, V. Monbet", https://doi.org/10.1016/j.csda.2020.107062.
 URL:

Contact:
Thi Tuyet Trang Chau
6.1.4 NHMSAR

Name:
NonHomogeneous Markov Switching Autoregressive Models

Keyword:
Statistical learning

Functional Description:
Calibration, simulation, validation of (non)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations. Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.
 URL:

Contact:
Valérie Monbet
6.1.5 3D Winds Fields Profiles

Keywords:
3D modeling, Opticflow, Atmosphere

Functional Description:
The algorithm computes 3D Atmospheric Motion Vectors (AMVs) vertical profiles, using incomplete maps of humidity, temperature and ozone concentration observed in a range of isobaric levels. The code is implemented for operational use with the Infrared Atmospheric Sounding Interferometer (IASI) carried on the MetOp satellite.
 URL:

Contact:
Patrick Heas
6.1.6 Screening4SLOPE

Keyword:
Optimization

Functional Description:
This software provides optimization routines to solve the SLOPE problem by exploiting "safe screening" reduction techniques.
 URL:
 Publication:

Contact:
Cedric Herzet

Participant:
Clement Elvira
7 New results
7.1 Objective 1 – Rare events and Monte Carlo simulation
MonteCarlo simulation
Participants: Frédéric Cérou, Patrick Héas, Mathias Rousset, François Ernoult.
In 7, 8, we quantify the robustness of a trained network to input uncertainties with a stochastic simulation and a statistical hypothesis test: the network is deemed as locally robust if the estimated probability of failure is lower than a critical level. The procedure is based on an Importance Splitting simulation generating samples of rare events. We derive theoretical guarantees that are nonasymptotic w.r.t. sample size. Experiments tackling large scale networks outline the efficiency of our method making a low number of calls to the network function.
7.2 Objective 2 – New topics in particle filtering
Multitarget tracking in track–before–detect context
Participants: Audrey Cuillery, François Le Gland, Valérie Monbet.
This work 6, 9 introduces a new class of particle filters, that include an auxiliary Markov transition in their design. This approach can be seen as an extension of the auxiliary particle filter. A prototypical situation where crossover could be useful is multitarget tracking. Indeed, it may happen that some targets in a multitarget particle are good proxies, but are not going to be selected just because the other targets in the same multitarget particle are bad proxies. This is unfair, and a better design would be to produce shuffled multitarget particles such that the particle for each different target can be replicated from a different multitarget particle.
7.3 Objective 3 – Semiparametric statistics
Participants: Valérie Monbet, Cédric Herzet, Thu Le Tran, Said Obakrim.
MIR spectroscopy is becoming an increasingly important tool potentially useful for diagnosis purposes especially by studying body fluids. However, such changes can be difficult to capture if the structure of the data is not considered. Our objective in 3 was to improve MIR spectra analysis by using approximation of the spectra by Bsplines at different specific resolutions and to combine these spectra representations with a machine learning model to predict hepatic steatosis from serum study.
7.4 Objective 4 – Model Reduction and Sparsity
Participants: Patrick Héas, Cédric Herzet, Théo Guyard.
In the context of model reduction, an issue is to find fast algorithms to project onto lowdimensional, sparse models.
2 studies the linear approximation of highdimensional dynamical systems using lowrank dynamic mode decomposition. Searching this approximation in a datadriven approach is formalized as attempting to solve a lowrank constrained optimization problem. This problem is nonconvex, and stateoftheart algorithms are all suboptimal. This paper shows that there exists a closedform solution, which is computed in polynomial time, and characterizes the ${\ell}^{2}$norm of the optimal approximation error.
Another avenue of research has been the study of the sparse surrogate in the context of “continuous’’ dictionaries, where the elementary signals forming the decomposition catalog are functions of some parameters taking their values in some continuous domain. In this context, we contributed to the theoretical characterization of the performance of a wellknown algorithmic procedure, namely “orthogonal matching pursuit’’ (OMP). More specifically, we proposed the first theoretical analysis of the behavior of OMP in the continuous setup, see 1.
In 4, we address the problem of approximating the atoms of a parametric dictionary, commonly encountered in the context of sparse representations in "continuous" dictionaries. We focus on the case of translationinvariant dictionaries. We derive necessary and sufficient conditions characterizing the existence of an "interpolating" and "translationinvariant" lowrank approximation.
5 deals with the sensor placement problem for an array designed for source localization. When it involves the identification of a few sources, the compressed sensing framework is known to find directions effectively thanks to sparse approximation. The present contribution intends to provide an answer to the following question: given a set of observations, how should we make the next measurement to minimize (some form of) uncertainty on the localization of the sources?
In 10, we propose a methodology to accelerate the resolution of the socalled “Sorted LOne Penalized Estimation” (SLOPE) problem. Our method leverages the concept of “safe screening”, wellstudied in the literature for groupseparable sparsityinducing norms, and aims at identifying the zeros in the solution of SLOPE.
In 11, we present a novel screening methodology to safely discard irrelevant nodes within a generic branchandbound (BnB) algorithm solving the ${\ell}_{0}$penalized leastsquares problem. Our contribution is a set of two simple tests to detect sets of feasible vectors that cannot yield optimal solutions. This allows to prune nodes of the BnB exploration tree, thus reducing the overall solution time.
In 12, we propose a procedure to accelerate the resolution of the wellknown "ElasticNet" problem. Our procedure is based on the (partial) identification of the solution support and the reformulation of the original problem into a problem of reduced dimension. The identification of the support leverages the novel concept of "safe relaxing" where one aims at identifying nonzero coefficients of the solution. It can be viewed as a dual approach to "safe screening" introduced in the last decade and allowing to reduce the problem dimension using the identification of zero coefficients of the solution. We show numerically that combining both methodologies in a "Screen And Relax" strategy enables to significantly improve the tradeoff between complexity and accuracy achievable by standard resolution techniques.
8 Bilateral contracts and grants with industry
8.1 Bilateral contracts with industry
Participants: Valérie Monbet, Patrick Héas.
8.2 Preliminary collaboration
Patrick Héas is collaborating with the agency European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) of Darmstadt. The transfer focuses on the estimation of atmospheric 3D winds from the future hyperspectral instruments.
9 Partnerships and cooperations
Participants: Cédric Herzet.
9.1 National initiatives
9.1.1 ANR
ANR Melody (20202024): Bridging geophysics and MachinE Learning for the modeling, simulation and reconstruction of Ocean DYnamics.
Cédric Herzet is part of the MELODY project. The MELODY project aims to bridge the physical model‐driven paradigm underlying ocean/atmosphere science and AI paradigms with a view to developing geophysically‐sound learning‐based and data‐driven representations of geophysical flows accounting for their key features (e.g., chaos, extremes, high‐dimensionality).
10 Dissemination
Participants: Mathias Rousset, Valérie Monbet, François Le Gland, Cédric Herzet, Patrick Héas, Frédéric Cérou.
10.1 Promoting scientific activities
10.1.1 Invited talks
Cédric Herzet (with Clément Elvira):
 Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem at Slope Conference.
Mathias Rousset:
 Rare Event Simulation of Neutron Trajectories at Bath University's Neutron Transport Days
 Rare Event Simulation with Adaptive Splitting at RESIM
10.2 Teaching  Supervision  Juries
10.2.1 Teaching
Cédric Herzet has given:
 INSA RENNES, 5ième année de l’option Génie Mathématique, cours de Parcimonie en traitement du signal et des images, 16h de cours magistraux + responsable du module
 Ensai RENNES, Master international « Smart Data » , cours « Foundations of Smart Sensing », 15h de cours magistraux
 Ensai RENNES, Master international « Smart Data » , cours « Advanced topics in Smart Sensing » , 3h de cours magistraux
 ENS RENNES, Master 1, cours « Traitement du signal » , 10h de cours magistraux
 Ensai RENNES, Master 2, suivi de projets, 15h
François Le Gland has given
 a 2nd year course on introduction to stochastic differential equations, at INSA (institut national des sciences appliquées) Rennes, within the cursus in applied mathematical,
 a 3rd year course on Bayesian filtering and particle approximation, at ENSTA (école nationale supérieure de techniques avancées), Palaiseau, within the statistics and control module,
 a 3rd year course on linear and nonlinear filtering, at ENSAI (école nationale de la statistique et de l'analyse de l'information), Ker Lann, within the statistical engineering track.
Mathias Rousset has given a 24h specialized course 'Large Deviations Theory' in Master 2 Fundamental Mathematics, Univ Rennes.
Patrick Héas has given a 28h class on algorithms and complexity at ESIR (Ecole Supérieure d'Ingénieurs de Rennes), first year.
10.2.2 Juries
François Le Gland has been a reviewer for the PhD thesis of Camille Palmier (université de Bordeaux, advisor: Pierre Del Moral).
Mathias Rousset has been the reviewer for the PhD thesis of Alessandro Andrea Barp (Imperial College), advisor: Mark Girolami.
10.2.3 Supervision
Internships
 Cédric Herzet has supervised one Master 1 (IRMAR) internship (Jules Berry). Subject: sparsity.
 Patrick Héas has supervised one Master 1 (ENSAE) internship (Hala Bouzidi). Subject: Langevin Markov Chain Monte Carlo in high dimensional Bayesian statistics.
PhDs
Cédric Herzet has supervised the PhDs of:
 Le Tran Thu, PhD, cosupervision with V. Monbet (Université de Rennes 1). UR1 funding. Subject: automatic diagnosis from spectral data using sparse representations in continuous dictionaries.
 Théo Guyard, PhD. INSA funding. Subject: optimization algorithms for sparse representations.
Mathias Rousset have supervised:
 François Ernoult, PhD (cosupervised with Fredéric Cérou). Funding: UR1 and Région Bretagne. Subject: small noise asymptotics of rare events Monte Carlo simulation algorithms
 Karim Tit, PhD, (cosupervised with Teddy Furon). Funding: CIFRE Thalès. Subject: statistical robustness of machine learning classifiers.
V. Monbet has also supervised:
 Le Thu Tran, PhD, Univ Rennes, Diagnosis Learning from Scarce Data with Sparse Representations in Continuous Dictionaries. Funding: 1/2 UR1 + 1/2 ANR AI4SDA
 Gabriel Jouan, PhD, Univ Rennes, Scalian, granted by CIFRE  Scalian. Cosupervision with A. Cuzol (UBS) et G. Monnier (Scalian).
 EssoRidah Bleza, PhD, Univ Bretagne Sud, Janasense, granted by CIFRE LIFY air. Cosupervision: PF Marteau (UBS)
 Said Obakrim, PhD, Univ Rennes, Ifremer (cosupervision: N. Raillard (Ifremer) et P. Ailliot (UBO)). Funding: 1/2 UR1 + 1/2 Ifremer.
11 Scientific production
11.1 Publications of the year
International journals
 1 articleWhen does OMP achieve exact recovery with continuous dictionaries?Applied and Computational Harmonic Analysis51March 2021, 39
 2 articleLowRank Dynamic Mode Decomposition: An Exact and Tractable Solution.Journal of Nonlinear Science3212021, article n°8
 3 articleMultiresolution Bsplines data compression improves MIR spectroscopybased Health diagnostic efficiency.Talenta Open4December 2021, 100063
International peerreviewed conferences
 4 inproceedingsInterpolating and translationinvariant approximations of parametric dictionaries.EUSIPCO 2020  28th European Signal Processing ConferenceAmsterdam, NetherlandsIEEEJanuary 2021, 20112015
 5 inproceedingsSequential Sensor Placement using Bayesian Compressed Sensing for Source Localization.EUSIPCO 2020  28th European Signal Processing Conference2021European Signal Processing ConferenceAmsterdam, NetherlandsIEEEJanuary 2021, 241245
 6 inproceedingsParticle filters with auxiliary Markov transition. Application to crossover and to multitarget tracking.Fusion 2021  24th International Conference on Information FusionRustenburg, South AfricaIEEENovember 2021
 7 inproceedingsEfficient Statistical Assessment of Neural Network Corruption Robustness.NeurIPS 2021  35th Conference on Neural Information Processing Systems34Advances in Neural Information Processing Systems proceedingsSydney (virtual), AustraliaDecember 2021
 8 inproceedingsÉvaluation statistique efficace de la robustesse de classifieurs.CAID 2021  Conference on Artificial Intelligence for DefenseActes de la conférence CAID 2021Rennes, FranceNovember 2021, 111
Reports & preprints
 9 miscParticle filters with auxiliary Markov transition. Application to crossover and to multitarget tracking.December 2021
 10 miscSafe rules for the identification of zeros in the solution of the SLOPE problem.October 2021
 11 miscNodescreening tests for l0penalized leastsquares problem with supplementary material.December 2021
 12 miscScreen & relax: accelerating the resolution of ElasticNet by safe identification of the solution support.December 2021