Multilayer approach

As for the first shallow water assumption, multi-layer systems were proposed to describe the flow as a superposition of Saint-Venant type systems 29, 32, 33. Even if this approach has provided interesting results, layers are considered separate and non-miscible fluids, which implies strong limitations. That is why we proposed a slightly different approach 30, 31 based on a Galerkin type decomposition along the vertical axis of all variables and leading, both for the model and its discretisation, to more accurate results.

A kinetic representation of our multilayer model allows to derive robust numerical schemes endowed with crucial properties such as: consistency, conservativity, positivity, preservation of equilibria, ...  It is one of the major achievements of the team but it needs to be analyzed and extended in several directions namely:

• The convergence of the multilayer system towards the hydrostatic Euler system as the number of layers goes to infinity is a critical point. It is not fully satisfactory to have only formal estimates of the convergence and sharp estimates would provide an optimal number of layers.
• The introduction of several source terms due for instance to the Coriolis force or extra terms from changes of coordinates seems necessary. Their inclusion should lead to substantial modifications of the numerical scheme.
• Its hyperbolicity has not yet been proven and conversely the possible loss of hyperbolicity cannot be characterised. Similarly, the hyperbolic feature is essential in the propagation and generation of waves.

Non-hydrostatic models

The hydrostatic assumption consists in neglecting the vertical acceleration of the fluid. It is considered valid for a large class of geophysical flows but is restrictive in various situations where the dispersive effects (like wave propagation) cannot be neglected. For instance, when a wave reaches the coast, bathymetry variations give a vertical acceleration to the fluid that strongly modifies the wave characteristics and especially its height.

Processing an asymptotic expansion (w.r.t. the aspect ratio for shallow water flows) into the Navier-Stokes equations, we obtain at the leading order the Saint-Venant system. Going one step further leads to a vertically averaged version of the Euler/Navier-Stokes equations involving some non-hydrostatic terms. This model has several advantages:

• it admits an energy balance law (that is not the case for most dispersive models available in the literature),
• it reduces to the Saint-Venant system when the non-hydrostatic pressure term vanishes,
• it consists in a set of conservation laws with source terms,
• it does not contain high order derivatives.

Multi-physics modelling

The coupling of hydrodynamic equations with other equations in order to model interactions between complex systems represents an important part of the team research. More precisely, three multi-physics systems are investigated. More details about the industrial impact of these studies are presented in the following section.

• To estimate the risk for infrastructures in coastal zones or close to a river, the resolution of the shallow water equations with moving bathymetry is necessary. The first step consisted in the study of an additional equation largely used in engineering science: The Exner equation. The analysis enabled to exhibit drawbacks of the coupled model such as the lack of energy conservation or the strong variations of the solution from small perturbations. A new formulation is proposed to avoid these drawbacks. The new model consists in a coupling between conservation laws and an elliptic equation, like the Euler/Poisson system, suggesting to use well-known strategies for the analysis and the numerical resolution. In addition, the new formulation is derived from classical complex rheology models and allowed physical phenomena like threshold laws.
• Interaction between flows and floating structures is the challenge at the scale of the shallow water equations. This study requires a better understanding of the energy exchanges between the flow and the structure. The mathematical model of floating structures is very hard to solve numerically due to the non-penetration condition at the interface between the flow and the structure. It leads to infinite potential wave speeds that could not be solved with classical free surface numerical schemes. A relaxation model was derived to overcome this difficulty. It represents the interaction with the floating structure with a free surface model-type.
• If the interactions between hydrodynamics and biology phenomena are known through laboratory experiments, it is more difficult to predict the evolution, especially for the biological quantities, in a real and heterogeneous system. The objective is to model and reproduce the hydrodynamics modifications due to forcing term variations (in time and space). We are typically interested in phenomena such as eutrophication, development of harmful bacteria (cyanobacteria) and upwelling phenomena.

Data assimilation and inverse modelling

In environmental applications, the most accurate numerical models remain subject to uncertainties that originate from their parameters and shortcomings in their physical formulations. It is often desirable to quantify the resulting uncertainties in a model forecast. The propagation of the uncertainties may require the generation of ensembles of simulations that ideally sample from the probability density function of the forecast variables. Classical approaches rely on multiple models and on Monte Carlo simulations. The applied perturbations need to be calibrated for the ensemble of simulations to properly sample the uncertainties. Calibrations involve ensemble scores that compare the consistency between the ensemble simulations and the observational data. The computational requirements are so high that designing fast surrogate models or metamodels is often required.

In order to reduce the uncertainties, the fixed or mobile observations of various origins and accuracies can be merged with the simulation results. The uncertainties in the observations and their representativeness also need to be quantified in the process. The assimilation strategy can be formulated in terms of state estimation or parameter estimation (also called inverse modelling). Different algorithms are employed for static and dynamic models, for analyses and forecasts. A challenging question lies in the optimization of the observational network for the assimilation to be the most efficient at a given observational cost.

Non-hydrostatic scheme

The main challenge in the study of the non-hydrostatic model is to design a robust and efficient numerical scheme endowed with properties such as: positivity, wet/dry interfaces treatment, consistency. It must be noticed that even if the non-hydrostatic model looks like an extension of the Saint-Venant system, most of the known techniques used in the hydrostatic case are not efficient as we recover strong difficulties encountered in incompressible fluid mechanics due to the extra pressure term. These difficulties are reinforced by the absence of viscous/dissipative terms.

Space decomposition and adaptive scheme

In the quest for a better balance between accuracy and efficiency, a strategy consists in the adaptation of models. Indeed, the systems of partial differential equations we consider result from a hierarchy of simplifying assumptions. However, some of these hypotheses may turn out to be irrelevant locally. The adaptation of models thus consists in determining areas where a simplified model (e.g. shallow water type) is valid and where it is not. In the latter case, we may go back to the “parent” model (e.g. Euler) in the corresponding area. This implies to know how to handle the coupling between the aforementioned models from both theoretical and numerical points of view. In particular, the numerical treatment of transmission conditions is a key point. It requires the estimation of characteristic values (Riemann invariant) which have to be determined according to the regime (torrential or fluvial).

Asymptotic-Preserving scheme for source terms

Hydrodynamic models comprise advection and sources terms. The conservation of the balance between source terms, typically viscosity and friction, has a significant impact since the overall flow is generally a perturbation around an equilibrium. The design of numerical schemes able to preserve such balances is a challenge from both theoretical and industrial points of view. The concept of Asymptotic-Preserving (AP) methods is of great interest in order to overcome these issues.

Another difficulty occurs when a term, typically related to the pressure, becomes very large compared to the order of magnitude of the velocity. At this regime, namely the so-called low Froude (shallow water) or low Mach (Euler) regimes, the difference between the speed of the gravity waves and the physical velocity makes classical numerical schemes inefficient: firstly because of the error of truncation which is inversely proportional to the small parameters, secondly because of the time step governed by the largest speed of the gravity wave. AP methods made a breakthrough in the numerical resolution of asymptotic perturbations of partial-differential equations concerning the first point. The second one can be fixed using partially implicit scheme.

Multi-physics models

Coupling problems also arise within the fluid when it contains pollutants, density variations or biological species. For most situations, the interactions are small enough to use a splitting strategy and the classical numerical scheme for each sub-model, whether it be hydrodynamic or non-hydrodynamic.

The sediment transport raises interesting issues from a numerical aspect. This is an example of coupling between the flow and another phenomenon, namely the deformation of the bottom of the basin that can be carried out either by bed load where the sediment has its own velocity or suspended load in which the particles are mostly driven by the flow. This phenomenon involves different time scales and nonlinear retroactions; hence the need for accurate mechanical models and very robust numerical methods. In collaboration with industrial partners (EDF–LNHE), the team already works on the improvement of numerical methods for existing (mostly empirical) models but our aim is also to propose new (quite) simple models that contain important features and satisfy some basic mechanical requirements. The extension of our 3D models to the transport of weighted particles can also be here of great interest.

Optimisation

Numerical simulations are a very useful tool for the design of new processes, for instance in renewable energy or water decontamination. The optimisation of the process according to a well-defined objective such as the production of energy or the evaluation of a pollutant concentration is the logical upcoming challenge in order to propose competitive solutions in industrial context. First of all, the set of parameters that have a significant impact on the result and on which we can act in practice is identified. Then the optimal parameters can be obtained using the numerical codes produced by the team to estimate the performance for a given set of parameters with an additional loop such as gradient descent or Monte Carlo method. The optimisation is used in practice to determine the best profile for turbine pales, the best location for water turbine implantation, in particular for a farm.

7.1.1 Freshkiss

• Name:
  FREe Surface Hydrodynamics using KInetic SchemeS
• Keywords:
  Finite volume methods, Hydrostatic Navier-Stokes equations, Free surface flows
• Functional Description:
  Freshkiss3D is a numerical code solving the 3D hydrostatic and incompressible Navier-Stokes equations with variable density.
• Contact:
  Jacques Sainte Marie
• Participants:
  Fabien Souillé, Emmanuel Audusse, Jacques Sainte Marie, Marie-Odile Bristeau
• Partners:
  UPMC, CEREMA

7.1.2 TSUNAMATHS

• Keywords:
  Modeling, Tsunamis
• Functional Description:
  Tsunamaths is an educational platform aiming at simulating historical tsunamis. Real data and mathematical explanations are provided to enable people to better understand the overall process of tsunamis.
• URL:
  http://­tsunamath.­paris.­inria.­fr/
• Contact:
  Jacques Sainte Marie
• Participants:
  Emmanuel Audusse, Jacques Sainte Marie, Raouf Hamouda

7.1.3 Verdandi

• Keywords:
  HPC, Model, Software Components, Partial differential equation
• Functional Description:
  Verdandi is a free and open-source (LGPL) library for data assimilation. It includes various such methods for coupling one or several numerical models and observational data. Mainly targeted at large systems arising from the discretization of partial differential equations, the library is devised as generic, which allows for applications in a wide range of problems (biology and medicine, environment, image processing, etc.). Verdandi also includes tools to ease the application of data assimilation, in particular in the management of observations or for a priori uncertainty quantification. Implemented in C++, the library may be used with models implemented in Fortran, C, C++ or Python.
• URL:
  http://­verdandi.­gforge.­inria.­fr/
• Contact:
  Vivien Mallet
• Participants:
  Dominique Chapelle, Gautier Bureau, Nicolas Claude, Philippe Moireau, Vivien Mallet

7.1.4 Polyphemus

• Functional Description:

  Polyphemus is a modeling system for air quality. As such, it is designed to yield up-to-date simulations in a reliable framework: data assimilation, ensemble forecast and daily forecasts. Its completeness makes it suitable for use in many applications: photochemistry, aerosols, radionuclides, etc. It is able to handle simulations from local to continental scales, with several physical models. It is divided into three main parts:

  libraries that gather data processing tools (SeldonData), physical parameterizations (AtmoData) and post-processing abilities (AtmoPy),

  programs for physical pre-processing and chemistry-transport models (Polair3D, Castor, two Gaussian models, a Lagrangian model),

  model drivers and observation modules for model coupling, ensemble forecasting and data assimilation.

• URL:
  http://­cerea.­enpc.­fr/­polyphemus/
• Contact:
  Vivien Mallet
• Participants:
  Sylvain Dore, Vivien Mallet

7.1.5 Urban noise analysis

• Keyword:
  Environment perception
• Functional Description:
  This software processes mobile observations collected by the application Ambiciti (previously known as SoundCity). It can merge simulated noise maps with the mobile observations.
• Contact:
  Vivien Mallet

7.1.6 Freshkiss3D

• Keywords:
  Python, Cython, Navier-Stokes
• Functional Description:
  Tool for the numerical solution of free surface Navier-Stokes equations
• Publication:
  hal-01393147
• Contact:
  Jacques Sainte Marie
• Participants:
  Cedric Doucet, Apolline El Baz, Jacques Sainte Marie
• Partner:
  UPMC

8.1.1 Discrete entropy inequalities via an optimization process

Participants: Nina Aguillon, Emmanuel Audusse, Vivien Desveaux, Julien Salomon.

The solutions of hyperbolic systems may contain discontinuities. These weak solutions verify not only the original PDEs, but also an entropy inequality that acts as a selection criterion determining whether a discontinuity is physical or not. Obtaining a discrete version of these entropy inequalities when approximating the solutions numerically is crucial to avoid convergence to unphysical solutions or even unstability. However such a task is difficult in general, if not impossible for schemes of order 2 or more. In 6, we introduce an optimization framework that enables us to quantify a posteriori the decrease or increase of entropy of a given scheme, locally in space and time. We use it to obtain maps of numerical diffusion and to prove that some schemes do not have a discrete entropy inequality. A special attention is devoted to the widely used second order MUSCL scheme for which almost no theoretical results are known.

8.1.2 The Navier-Stokes system with temperature and salinity for free surface flows. Numerical scheme and validation

Participant: Jacques Sainte-Marie.

with Léa Boittin, François Bouchut, Marie-Odile Bristeau, Anne Mangeney, Fabien Souillé

In 7, we propose and study a numerical scheme for the Navier-Stokes-Fourier system derived and studied by the authors in Boittin at al. (2023). This system models hydrostatic free surface flows with density variations depending on temperature or salinity. We show that the finite volume/finite element scheme presented – based on a layer averaged formulation of the model – is well-balanced with regards to the steady state of the lake at rest and preserves the nonnegativity of the water height. A maximum principle on the density is also proved as well as a discrete entropy inequality (when the thermal and viscous effects are neglected). Some numerical validations are finally shown with comparisons to 3D analytical solutions and experiments.

8.1.3 Diagnostic of the Lévy area for geophysical flow models in view of defining high order stochastic discrete-time schemes

Participant: Pierre-Marie Boulvard.

with Etienne Mémin

In 8, we characterize numerically through two criteria the Lévy area related to unresolved fluctuation velocities associated to a stochastic coarse-scale representation of geophysical fluid flow dynamics. We study in particular whether or not the process associated to the random unresolved velocity components exhibits a Lévy area corresponding to a Wiener process, and if the law of this process can reasonably be approached by a centered Dirac measure. This exploration enables us to answer positively to a conjecture made for the constitution of high-order discrete time evolution schemes for stochastic representation defined from stochastic transport.

8.1.4 Acceleration of a wave-structure interaction solver by the Parareal method

Participant: Julien Salomon.

with Yohan Poirier, Aurélien Babarit, Pierre Ferrant, Guillaume Ducrozet

Potential flow theory-based solvers are commonly used in ocean engineering to investigate the interactions between ocean waves and floating bodies. Depending on assumptions, several methods have been proposed. Among them, the Weak-Scatterer method is an interesting trade-off in the sense that this approach is not limited in theory by the small wave amplitudes and small body motions assumptions of linear methods. Moreover, this approach is in practice more stable than the fully non-linear methods. An implementation of the Weak-Scatterer method is the WS-CN code (Letournel, 2015; Chauvigné, 2016; Wuillaume, 2019). The computational time of the WS-CN code which is considered in the present study is relatively long for engineering purposes. In order to reduce it, 13 presents an implementation of the Parareal method in the WS-CN code. The Parareal method is an algorithm for parallelizing a simulation in time that can accelerate the complete simulation (Lions, 2001) . This is a key difference in comparison to other acceleration techniques which have been studied in the literature (e.g. the Fast Multipole Method (FMM), the precorrected Fast Fourier Transform (pFFT) method, ). To the authors’ knowledge, the present study is the first to couple the Parareal method to a potential flow theory-based wave-structure interaction solver. It is shown that the method can significantly reduce the computational time for small wave steepness, but that the performance decreases rapidly with increasing steepness.

8.2.1 Hyperbolicity of a semi-Lagrangian formulation of the hydrostatic free-surface Euler system

Participants: Bernard Di Martino, Edwige Godlewski, Julien Guillod, Jacques Sainte-Marie.

with Chourouk El Hassanieh

By a semi-Lagrangian change of coordinates, the hydrostatic Euler equations describing free-surface sheared flows is rewritten in 11 as a system of quasilinear equations, where stability conditions can be determined by the analysis of its hyperbolic structure. The system one obtains can be written as a quasi linear system in time and horizontal variables and involves no more vertical derivatives. However, the coefficients in front of the horizontal derivatives include an integral operator acting on the new vertical variable. The spectrum of these operators is studied in detail, in particular it includes a continuous part. Riemann invariants are then determined as conserved quantities along the characteristic curves. Examples of solutions are provided, in particular stationary solutions and solutions blowing-up in finite time. Eventually, we propose an exact multilayer -discretization, which could be used to solve numerically this semi-Lagrangian system, and analyze the eigenvalues of the corresponding discretized operator to investigate the hyperbolic nature of the approximated system.

8.2.2 Well-posedness and potential-based formulation for the propagation of hydro-acoustic waves and tsunamis

Participant: Jacques Sainte-Marie.

with Juliette Dubois, Sébastien Imperiale, Anne Mangeney

In 12, we study a linear model for the propagation of acoustic and surface gravity waves in a stratified free-surface ocean. A formulation was previously obtained by linearizing the compressible Euler equations. In this paper, we introduce a new formulation written with a generalized potential. The new formulation is obtained by studying the functional spaces and operators associated to the model. The mathematical study of this new formulation is easier and the discretization is also more efficient than for the previous formulation. We prove that both formulations are well-posed and show that the solution to the first formulation can be obtained from the solution to the second. Finally, the formulations are discretized using a spectral element method, and we simulate tsunamis generation from submarine earthquakes and landslides.

8.2.3 A unified formulation of quasi-geostrophic and shallow water equations via projection

Participant: Louis Thiry.

with Long Li, Etienne Mémin, Guillaume Roullet

In 14, we introduces a unified model for layered rotating shallow-water (RSW) and quasi-geostrophic (QG) equations, based on the intrinsic relationship between these two sets of equations. We propose a novel formulation of the QG equations as a projection of the RSW equations. This formulation uses the same prognostic variables as RSW, namely velocity and layer thickness, thereby restoring the proximity of these two sets of equations. It provides direct access to the ageostrophic velocities embedded within the geostrophic velocities resolved by the QG equations. This approach facilitates the study of differences between QG and RSW using a consistent numerical discretization. We demonstrate the effectiveness of this formulation through examples including vortex shear instability, double-gyre circulation, and a simplified North Atlantic configuration

8.2.4 MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries

Participant: Louis Thiry.

with Long Li, Etienne Mémin, Guillaume Roullet

In 15, we present MQGeometry, a multi-layer quasi-geostrophic (QG) equation solver for non-rectangular geometries. We advect the potential vorticity (PV) with finite volumes to ensure global PV conservation using a staggered discretization of the PV and stream function (SF). Thanks to this staggering, the PV is defined inside the domain, removing the need to define the PV on the domain boundary. We compute PV fluxes with upwind-biased interpolations whose implicit dissipation replaces the usual explicit (hyper-)viscous dissipation. The discretization presented here does not require tuning of any additional parameter, e.g., additional eddy viscosity. We solve the QG elliptic equation with a fast discrete sine transform spectral solver on rectangular geometry. We extend this fast solver to non-rectangular geometries using the capacitance matrix method. Subsequently, we validate our solver on a vortex-shear instability test case in a circular domain, on a vortex–wall interaction test case, and on an idealized wind-driven double-gyre configuration in an octagonal domain at an eddy-permitting resolution. Finally, we release a concise, efficient, and auto-differentiable PyTorch implementation of our method to facilitate future developments on this new discretization, e.g., machine-learning parameterization or data-assimilation techniques.

8.3.1 A SPIRED code for the reconstruction of spin distribution

Participant: Julien Salomon.

with Simon Buchwald, Gabriele Ciaramella, Dominique Sugny

In Nuclear Magnetic Resonance (NMR), it is of crucial importance to have an accurate knowledge of the spin probability distribution corresponding to inhomogeneities of the magnetic fields. An accurate identification of the sample distribution requires a set of experimental data that is sufficiently rich to extract all fundamental information. These data depend strongly on the control fields (and their number) used experimentally to perturb the spin system. In 10, we present and analyze a greedy reconstruction algorithm, and provide the corresponding SPIRED code, for the computation of a set of control functions allowing the generation of data that are appropriate for the accurate reconstruction of a sample probability distribution. In particular, the focus is on NMR and spin dynamics governed by the Bloch system with inhomogeneities in both the static and radio-frequency magnetic fields applied to the sample. We show numerically that the algorithm is able to reconstruct non trivial joint probability distributions of the two inhomogeneous Hamiltonian parameters. A rigorous convergence analysis of the algorithm is also provided.

8.3.2 Gauss–Newton Oriented Greedy Algorithms for the Reconstruction of Operators in Nonlinear Dynamics

Participant: Julien Salomon.

with Simon Buchwald, Gabriele Ciaramella, Dominique Sugny

In 9, we present e convergence analysis of greedy reconstruction algorithms based on the strategy presented in [Y. Maday and J. Salomon, Joint Proceedings of the 48th IEEE Conference on Decision and Control and the 28th Chinese Control Conference, 2009, pp. 375–379]. These procedures allow the design of a sequence of control functions that ease the identification of unknown operators in nonlinear dynamical systems. The original strategy of greedy reconstruction algorithms is based on an offline/online decomposition of the reconstruction process and an ansatz for the unknown operator obtained by an a priori chosen set of linearly independent matrices. In the previous work [S. Buchwald, G. Ciaramella, and J. Salomon, SIAM J. Control Optim., 59 (2021), pp. 4511–4537], convergence results were obtained in the case of linear identification problems. We tackle here the more general case of nonlinear systems. More precisely, we introduce a new greedy algorithm based on the linearized system. We show that the controls obtained with this new algorithm lead to the local convergence of the classical Gauss–Newton method applied to the online nonlinear identification problem. We then extend this result to the controls obtained on nonlinear systems where a local convergence result is also proved. The main convergence results are obtained for dynamical systems with linear and bilinear control structures.

9.1.1 STIC/MATH/CLIMAT AmSud projects

9.1.2 Participation in other International Programs

• Title:
  OceanIA
• Program:
  Defi INRIA
• Duration:
  November 1, 2020 – December 31, 2025
• Local supervisor:
  Julien Salomon
• Partners:
  • Inria Chile
  • Inria Sophia
• Inria contact:
  Julien Salomon
• Summary:
  OcéanIA is a four-years project (11.2020–12.2025) involving Inria teams in Chile, Paris, Saclay, and Sophia-Antipolis, and the Fondation Tara Océan, the Center of Mathematical Modeling (CMM, U.Chile), the Pontificia Universidad Católica de Chile (PUC), the GO-SEE CNRS Federation, and the Laboratoire des Sciences du Numérique de Nantes (LS2N). See the full description of the team here.

9.2.1 Visits of international scientists

Projet Emergence (2023-2025)

Participants: Nathalie Ayi.

• Project acronym: Emergence
• Project title: Numerical studies of STOChastic Kinetic partial differential equations (STOCK)
• Coordinator: Nathalie Ayi (SU)
• Funding: 15 000 euros.

ANR NASSMOM (2024-2028)

Participants: Nina Aguillon, Julie Deshayes, Sybille Techené.

• Project acronym: NASSMOM
• Project title: Nondiffusive advection schemes and spurious mixing in ocean model
• Coordinator: Nina Aguillon
• Funding: 246 957 euros.

Water masses of given temperature and salinity are advected without much blending over long periods of time in the ocean. At the numerical level, the discretization of the equation creates a spurious mixing (or numerical diffusion) that artificially mixes the water masses. It can be of the same order of magnitude as the physical mixing. This is especially true in climate simulations where the grid is coarse and the time of integration is long. This project is concerned with two different aspects of the spurious mixing. First, we will study a numerical procedure to quantify precisely in space and time the spurious mixing. The method is both different from usual entropy inequality in maths that are limited to first order schemes, and from global diagnosis based on water mass transformation in oceanography. It should allow us to better understand the geography and effects of spurious mixing. It can be applied to any transported quantity or to the evolution of total energy. Second, we will develop antidiffusive advection schemes for the salinity and temperature in the European ocean general circulation model NEMO. This approach is different from the usual « higher order, finer grids » strategy and has been successfully employed for atmospheric pollution and biphasic flows. Preliminary results obtained by the principal investigator on a new second order antidiffusive scheme show a gain of accuracy in the smooth regions and a correct behavior in 2 space dimensions. This is a major improvement compared to the existing first order antidiffusive schemes, which should allow us to go beyond its use in the vertical direction only. In conclusion, this project proposes a mathematical approach on the question of spurious mixing in ocean general circulation models. The team gathers experts on numerical analysis and ocean modeling. We will collaborate closely together with the aim of having a direct contribution to NEMO.

Nat. ANR AIAI 2023-2026 N. Jourdain (CNRS) JD CNRS, CEA 602 k€ Artificial Intelligence to improve the coupling between the Antarctic ice sheet and the ocean/atmosphere system

ANR AIAI (2023-2026)

Participant: Julie Deshayes.

• Project acronym:AIAI
• Project title: Artificial Intelligence to improve the coupling between the Antarctic ice sheet and the ocean/atmosphere
• Coordinator: N. Jourdain
• Funding: 567301 euros.

The fate of the Antarctic ice sheet is the largest source of uncertainty in future sea level projections. The magnitude and sign of the Antarctic contribution indeed results from the compensation of two opposite effects: increased surface mass gain and increased ocean-induced dynamical mass loss, both of which are highly uncertain. A large part of the uncertainty on these opposite effects comes from the absence of coupling between ice sheet and ocean/atmosphere models, which has motivated the recent development of such coupled models. However, there is currently a mismatch between the coarse resolution of ocean/atmosphere models and the high resolution needed for ice sheet models near their grounding lines and at their edge.

In this project, we aim to improve the integration of the Antarctic ice sheet into an Earth System Model through the use of neural networks at the coupling interfaces. These will bring increased resolution and account for polar processes absent or poorly represented in Earth System Models (e.g., surface melt and runoff, ice-shelf basal melt). Neural networks will be trained on high-resolution polar-oriented atmospheric and oceanic simulations, including in a warmer climate and with modified ice sheet geometry. Members of our consortium have recently conducted two pilot studies on neural networks that serve as proofs of concept for this project.

The two neural networks will be used at the interface between the Elmer/Ice ice sheet model and the ocean and atmosphere components of IPSL-CM6-LR. We will assess the model’s ability to reproduce the observed evolution of the Antarctic ice sheet. We will also run projections to 2100 with uncertainty constrained by the newly developed neural network interfaces. The proposed coupling through neural network interfaces will be applicable to other climate models, including those that have recently been coupled without refined interface, as well as to standalone ice sheet models.

Our project is in line with the “Artificial Intelligence to address societal challenges” part of the French strategy regarding artificial intelligence, and will have strong societal impact (coastal management) through the improvement of sea level rise projections.

Nat. ANR BOURGEONS 2023-2027 A.L. Dalibard ALD, CG Université de Grenoble, Université de Bordeaux 424k€

ANR BOURGEONS (2023-2027)

Participant: Anne-Laure Dalibard.

• Project acronym:BOURGEONS
• Project title: Boundaries, Congestion and Vorticity in Fluids: A connection with environmental issues
• Coordinator: Anne-Laure DALIBARD
• Funding: 567301 euros.

The purpose of the BOURGEONS project is to investigate several aspects of fluid dynamics which all play an important role in geophysical flows and their environmental applications. It is organized around two main topics which strongly expanded in recent years, and for which we will address both fundamental and applied aspects: (i) the dynamics of floating objects, congested flows and extreme waves; and (ii) the analysis of boundary layers and vortices.

PEPR Climaths (2024-2029)

Participant: Anne-Laure Dalibard.

• Project acronym: CLIMATHS
• Project title: Fundamental advances in modelling key processes for reducing climate change impacts
• Coordinator: Anne-Laure DALIBARD
• Funding: 1 000 000 euros.

The CLIMATHS project targets fundamental developments required to reduce uncertainties in the study of the impacts of climate change. The climate and its main components - including the atmosphere and ocean - obey complex dynamics, some aspects of which are still poorly understood, despite their crucial importance for mankind in a context of climate disruption.

ANR ALLOWAPP (2019-2024)

Participants: Julien Salomon.

• Project acronym: ALLOWAPP
• Project title: Algorithmes pour l'optimisation à grande échelle de problèmes de propagation d'ondes
• Coordinator: Laurence Halpern (Université Paris-Nord)
• Funding: 317 891 euros.

The goal of the ALLOWAPP project is the design of space-time parallel algorithms for large-scale optimization problems associated with wave propagation phenomena. Such problems appear in seismology, geophysics, but also in various applications from data assimilation. The large amount of data and the volume of computations required for the accurate numerical solution of wave propagation problems, within an optimization loop, requires the use of massively parallel computers. Time-parallel methods have experienced a great development in the last ten years, and for parabolic problems an almost perfect efficiency for a large number of processors has been achieved (scalability). It is quite different for wave propagation problems. In this project, we propose to develop robust, efficient and scalable methods for space-time parallelization of these optimization problems.

ANR GeoFun (2020-2024)

Participants: Nina Aguillon.

• Project acronym: GeoFun
• Project title: Ecoulements géophysiques avec des modèles unifiés
• Coordinator: Martin Parisot (INRIA Bordeaux Sud-Ouest)
• Funding: 524 880 euros.

The GeoFun project aims to improve the modeling and simulation of geophysical flows involving at least two different processes. Numerical simulation of watersheds and estimation of water resources is the main application of the project's achievements. In this context, a free surface flow (rivers, lakes) is the upper part of a groundwater flow (water table). Our vision of river transport is often naive, because we think first of rivers, lakes and floods, but in reality, 80 % of the water of the continents is underground. Sometimes, the porous substratum is covered by an impermeable rock stratum, which confines the flow as in pipes, except for some points where springs and resurgences appear.

ANR Saphir (2022-2024)

Participants: Jacques Sainte-Marie, Bernard Di Martino.

• Project acronym: Saphir
• Project title: Sensor Augmented weather Prediction at high Resolution
• Coordinator: J-F. Muzy (Université de Toulouse Paul Sabatier)
• Funding: 296 000 euros.

ANR DEEPNUM (2022-2026)

Participants: Julien Salomon, Jacques Sainte-Marie.

• Project acronym: DEEPNUM
• Project title: Algorithmes pour l'optimisation à grande échelle de problèmes de propagation d'ondes
• Coordinator: Julien Salomon
• Funding: 493 799,20 euros.

The project aims at developing the interplay between Deep Neural Networks (DNNs) and Differential Equations (DEs), with the goal of modeling complex dynamical systems arising from the observation of natural phenomena. Two application domains are targeted, environment and healthcare. We address three fundamental questions: how to adapt and apply numerical analysis theory to DNNs for analyzing them, providing theoretical guaranties and improving their robustness, how to combine simulation and data based models into hybrid systems, how could DNNs help solving DEs and complement numerical solvers. In addition, we evaluate our methods on simulation and real world data in the environment and health domains. DeepNuM gathers partners with complementary skills: DEs and Environment (INRIA-ANGE), Machine Learning and DNNs (Sorbonne – MLIA), DEs and Biophysics (INRIA-EPIONE).

ANR MEGA (2023-2028)

Participants: Bernard Di Martino, Jacques Sainte-Marie, Nina Aguillon.

• Project acronym: MEGA
• Project title: Giant submarine landslides in gas hydrate provinces: a comparison of the Nile and Amazon deep-sea fans
• Coordinator: Sébastien Mingeon
• Funding: 533,348 euros.

Giant submarine landslides (10-2000 km3) are found in the thick Quaternary sediment succession of passive continental margins. Their ages coincide with periods of sea-level fall and rise, but it is unclear how such vast failures can be triggered on low seafloor slopes (<2?) in the absence of a triggering factor such as seismicity. Key hypotheses involve excess pore pressures linked to reductions in gas-hydrate stability, driven by changes either in climate or in subsurface fluid flow. The MEGA project wants to explore such hypotheses through the first modelling of linked changes in gas hydrate and slope stability in response to ocean pressure and temperature changes, using an innovative comparison of the Nile and Amazon deep-sea fans that experience different forms of climate forcing over glacial-interglacial timescales. As such megaslides have never triggered in historical times, MEGA will provide input for the first modelling of their tsunamogenic consequences on coastal zones.

RT "Terre & Énergies" (2023–)

Participants: Emmanuel Audusse, Bernard di Martino, Martin Parisot, Jacques Sainte-Marie.

The MathGeoPhy interdisciplinary research group was created in January 2022, for five years. It is funded by the French National Center for Scientific Research (CNRS), with the mission of animating the French scientific community around the theme of mathematics in interaction with the geophysics of fluid and solid envelopes. The members of the GdR are interested in mathematical modeling, scientific computing and the development of new numerical methods applied in particular to :

• offshore and coastal ocean dynamics, gravity waves, coastal erosion problems
• micro-macro approaches, granular and complex flows
• fluvial and torrential hydrodynamics, extreme events and environmental risks, landslides, avalanches, volcanic eruptions, glaciology, etc.

GdR EOL-EMR (2021–2026)

Participants: Julien Salomon, Jacques Sainte-Marie.

The objectives of this project are the following:

• To promote the dissemination of existing knowledge and expertise within and across disciplines.
• The GDR EMR is a forum for the exchange of expertise and know-how within and across disciplines.
• To promote the implementation of collaborations, between partners of the GDR and with the industrial fabric.

The GDR is an entry and orientation point. It provides a forum for the exchange of information concerning industrial needs and the kills of the academic community; and enables the bringing together of players. Valuing the national scientific community The GDR EMR gives visibility to the community, in particular through the development of a mapping of the actors and themes available on the web platform

10.1.1 Scientific events: organisation

Participants: Julien Salomon, Julien Guillod, Nina Aguillon, Anne-Laure Dalibard.

ANGE members took part to the organization of the following scientific events and meetings.

• JS organized (24.01, 14.06, 15.10, 06.10) four joint-meetings on EMR (INRIA/IFPEN)
• JS Co-organized (27.06) a Workshop on time parallelization
• JS Co-organized the INRIA-LJLL bi-monthly Seminar "Rencontres INRIA-JLL en analyse numérique et calcul scientifique"
• JG Co-organized Séminaire mensuel Séminaire "Analyse non-linéaire et EDP"
• JG Co-organized Séminaire mensuel Séminaire Infomath
• NAgui Co-organized 2021- Journée interne du LJLL
• NAgui Co-organized the 2023 ed. of NUMHYP
• ALD is member of the organizing committee of LJLL seminar
• ALD is member of CNFM (Centre interuniversitaire de microélectronique et nanotechnologies)

10.1.2 Editorial activities

Participants: Julien Salomon, Emmanuel Audusse, Anne-Laure Dalibard.

Edition activities of the team are the following.

• 2022-...: ALD is Co-Editor in chief of Annales de l'Institut Henri Poincaré - Analyse non linéaire
• 2020-...: ALD is editor in SIAM Journal on Mathematical Analysis
• 2021-...: JS is editor in chief of MATAPLI (national journal of the applied maths community). EA is responsible for its section "Du côté des écoles d'ingénieurs".

10.1.3 Invited talks

10.1.4 Leadership within the scientific community

Participants: Emmanuel Audusse.

EA is adjoint director of GDR MathGeoPhy (2022-)

10.1.5 Scientific expertise

Participants: Julien Salomon.

• 05-10.2024: GT d'instruction de la proposition d'équipe-projet commune CASTOR (Member)
• 06-11.2024: GT d'instruction de la proposition d'équipe-projet commune ANGUS (Member)
• 2024: Comité de sélection MdC, IRMAR, Université Rennes 2 (Member)
• 2024: concours CRCN ISFP du centre INRIA Nancy (Member)
• 2024: concours CRCN ISFP du centre INRIA Bordeaux (Member)
• 2018-: INRIA "Commission de Emplois Scientifiques" (Member)
• 2023-2027: INRIA "Commission d'évaluation" (Elected Member)

Participants: Jacques Sainte-Marie.

• 10-11/12/2024: Jury de recrutement CRCN INRAE (member)
• 19-20/11/2024: Jury de recrutement DR2 INRAE (member)

Participants: Nina Aguillon.

• 2024: concours CRCN ISFP du centre INRIA Paris
• 2024: comité de sélection MdC, LIP6, Sorbonne Université
• 2018-: INRIA Comission de Emplois Scientifiques (Member)

Participants: Anne-Laure Dalibard.

• 2024: Comité de sélection pour un poste de professeur à l'Université Paris-Saclay (membre)
• 2024: Comité de sélection pour un poste de MCF à l'Université Sorbonne Paris-Nord (présidente)

Participants: Julien Guillod.

2023-2024: Co-responsable de la création du nouveau site internet du LJLL

Participants: Nathalie Ayi.

2023 : Membre nommée au CNU 26.

Participants: Jacques Sainte-Marie.

2020-2024: External advisory board - ERC Synergy

10.1.6 Research administration

Participants: Julien Salomon.

2023-...: Membre élu du CSA de l'INRIA

Participants: Jacques Sainte-Marie.

• 2019-... : directeur scientifique adjoint
• 2022-2028 : Co-pilote du PEPR 'agroécologie et numérique'
• 2022-... : Responsable du programme 'Numérique et environnement'

Participants: Nathalie Ayi.

• 2020 - ... : Membre du conseil du laboratoire LJLL
• 2020 - ... Membre du comité scientifique de l'UFR 929

Participants: Emmanuel Audusse.

2020-2024 Membre des CR et CAC de USPN (UNIV. PARIS XIII)

Participants: Nina Aguillon.

2022- ... : Membre du conseil de l'UFR 929, SU

Participants: Julie Deshayes.

2024-... : co-directrice du Centre de Modelisation du Climat (IPSL)

Participants: Anne-Laure Dalibard.

• 2019-2025 : Membre du Conseil d'administration de la SMAI
• 2022-... : Membre du Conseil scientifique de l'Institut Pascal
• 2020-... : Membre du Conseil scientifique et bureau du GDR "Défis théoriques pour les sciences du climat"
• 2021-... : Membre du Conseil scientifique du Réseau Thématique (anciennement GDR) Analyse des EDP
• 2024-... : Membre du comité de programme du PEPR Maths-Vives

10.1.7 Faculty administration

Participants: Julien Guillod.

• 2019-2027 : Membre du Conseil de la licence de mathématique de Sorbonne Université
• 2022-... : Référent égalité et luttre contre les discriminations pour l'UFR 929, SU
• 2022-2025 : Membre du conseil de l'UFR 929, SU

Participants: Nina Aguillon.

• 2022-... : membre du conseil de département du cycle d'intégration
• 2022-... : comité de pilotage de CAPSULE (centre d'accompagnement à la pédagogie et support à l'expérimentation)

10.2.1 Teaching

EG is the president of commission française pour l'enseignement des mathématiques (CFEM)

Teaching activities of ANGE are summarized in the following.

• Participants: Julien Salomon.

  • Méthodes numériques pour des modèles incluants des EDP, 45,M2,Université d'Abomey-Calavi, Bénin CM

• Participants: Jacques Sainte-Marie.

  • Modélisation des écoulements gravitaires 40H, M1, Univ. Paris-Diderot et IPGP
  • Méthodes numériques en géosciences 50H, M2, Univ. Paris-Diderot et IPGP

• Participants: Nathalie Ayi.

  • Approximation des EDPs 36H, M1, CM, Sorbonne Université
  • EDO 40H, L2, CM, Polytech Sorbonne

• Participants: Julie Deshayes.

  • Structures mathématiques 22H, L3, TD, Polytech Sorbonne

• Participants: Nina Aguillon.

  • Directrice des études de L2 mathématiques, 64H, L2, , responsabilité, Sorbonne Université
  • Topologie et calcul différentiel 1, 36H, L2, , TD
  • Modèles hyperboliques d'écoulements complexes dans le domaine de l'environnement, 10H, M2, CM, Sorbonne Université
  • Formation des nouveaux moniteurs en mathématiques 8H, D, responsabilité, Sorbonne Université
  • EDP elliptiques 40H, M1, TD, Sorbonne Université,

• Participants: Bernard Di Martino.

  • Calcul différentiel 54H, L3, CM,TD, Université de Corse,
  • Pratiques d'Algèbre 18H, L1, TP, Université de Corse,
  • Pratique d'Analyse 18H, L1, TP, Université de Corse,
  • Pratique d'Algèbre 18H, L2, TP, Université de Corse,
  • Pratique d'Analyse 18H, L2, TP, Université de Corse,
  • Modélisation 15H, M2, CM, Université de Corse

• Participants: Emmanuel Audusse.

  • Optimisation, 30H, ING2, USPN, TD-TP
  • Calcul scientifique, 30H, L2, USPN, CM-TD-TP

• Participants: Julien Guillod.

  • Programmation Python pour les mathématiques, 45H, L2, TP, Sorbonne Université,
  • Fondements des méthodes numériques, 48H, M1, TD, Sorbonne Université,
  • Topologie et Calcul différentiel, 63H, L3, CM, ENS Paris

• Participants: Anne-Laure Dalibard.

  • Intégration et probabilités 54H L3 CM ENS Paris
  • Mathématiques pour les études scientifiques 54H L1 CM Sorbonne Université
  • Colles 10H L3 Colles Sorbonne Université

10.2.2 Supervision

• Participants: Julien Salomon.

  • M2, Ghislain Bognon, SU, 04-08.24, Analyse d'une méthode vortex pour la simulation de turbine
  • (avec Sever Hirstoaga) M2, Credo Fanou, SU, 04-08.24, Analyse d'erreur d'un modèle réduit en phys. des plasmas
  • PhD, Lucas Perrin, SU, 2021-2024, Parallélisation en temps et assimilation de données
  • PhD, Norbert Tognon, SU, 2022-2025, Techniques d'accélération pour le contrôle optimal
  • (avec Patrick Armand) PhD, Allan Gouvenaux, CEA, 2023-2024, Méta-modélisation pour la simulation rapide de phénomènes de transport

• Participants: Emmanuel Audusse.

  • F. Benkhaldoun, PhD, Laila Baroukh, USPN, 2021-2024, Simulation numérique pour des écoulements partiellement congestionnés avec rhéologie complexe

• Participants: Julie Deshayes.

  • PhD, David Kamm, SU, 2022-2025, Ameliorer les parametrisations des processus de fine echelle pour réduire les incertitudes des modèles de climat
  • (with Anne-Laure Dalibard) PhD, Corentin Gentil, SU, 2023-2026, Courants oceaniques de bord ouest, analyse theorique et numerique

• Participants: Nina Aguillon.

  • PhD & M2, Marie Boussard, Quantification de diffusion numérique dans les modèles de circulation océanique

• Participants: Nathalie Ayi, Jacques Sainte-Marie.

  • Pedro Ramaciotti Morales (SciencesP Paris) PhD, Francesco Cornia, SU, 2024-2027, Opinion dynamics modelling

• Participants: Anne-Laure Dalibard.

  • M2, Philomène Dufour, SU, 2024, Comportement non-local pour un problème de couches limites océan-atmosphère

10.2.3 Juries

International journals

• 6 articleN.Nina Aguillon, E.Emmanuel Audusse, V.Vivien Desveaux and J.Julien Salomon. Discrete entropy inequalities via an optimization process.ESAIM: Mathematical Modelling and Numerical Analysis581January 2024, 363-391HALDOIback to text
• 7 articleL.Léa Boittin, F.François Bouchut, M.-O.Marie-Odile Bristeau, A.Anne Mangeney, J.Jacques Sainte-Marie and F.Fabien Souillé. The Navier-Stokes system with temperature and salinity for free surface flows. Numerical scheme and validation.Journal of Computational Physics510August 2024, 113065HALDOIback to text
• 8 articleP.-M.Pierre-Marie Boulvard and E.Etienne Mémin. Diagnostic of the Lévy area for geophysical flow models in view of defining high order stochastic discrete-time schemes.Foundations of Data Science612024, 1-21HALDOIback to text
• 9 articleS.Simon Buchwald, G.Gabriele Ciaramella and J.Julien Salomon. Gauss–Newton Oriented Greedy Algorithms for the Reconstruction of Operators in Nonlinear Dynamics.SIAM Journal on Control and Optimization623May 2024, 1343-1368HALDOIback to text
• 10 articleS.Simon Buchwald, G.Gabriele Ciaramella, J.Julien Salomon and D.Dominique Sugny. A SPIRED code for the reconstruction of spin distribution.Computer Physics Communications299June 2024, 109126HALDOIback to text
• 11 articleB.Bernard Di Martino, C.Chourouk El Hassanieh, E.Edwige Godlewski, J.Julien Guillod and J.Jacques Sainte-Marie. Hyperbolicity of a semi-Lagrangian formulation of the hydrostatic free-surface Euler system.Nonlinearity3812025, 015018In press. HALDOIback to text
• 12 articleJ.Juliette Dubois, S.Sébastien Imperiale, A.Anne Mangeney and J.Jacques Sainte-Marie. Well-posedness and potential-based formulation for the propagation of hydro-acoustic waves and tsunamis.ESAIM: Mathematical Modelling and Numerical AnalysisApril 2024HALDOIback to text
• 13 articleY.Yohan Poirier, J.Julien Salomon, A.Aurélien Babarit, P.Pierre Ferrant and G.Guillaume Ducrozet. Acceleration of a wave-structure interaction solver by the Parareal method.Engineering Analysis with Boundary Elements167October 2024, 105870HALDOIback to text
• 14 articleL.Louis Thiry, L.Long Li, E.Etienne Mémin and G.Guillaume Roullet. A unified formulation of quasi-geostrophic and shallow water equations via projection.Journal of Advances in Modeling Earth SystemsSeptember 2024HALDOIback to text
• 15 articleL.Louis Thiry, L.Long Li, G.Guillaume Roullet and E.Etienne Mémin. MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries.Geoscientific Model Development174February 2024, 1749-1764HALDOIback to text

International peer-reviewed conferences

• 16 inproceedingsS.Sarra Khairi, E.Etienne Meunier, R.Renaud Fraisse and P.Patrick Bouthemy. Efficient local correlation volume for unsupervised optical flow estimation on small moving objects in large satellite images.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)CVPRW 2024 - IEEE/CVF Conference on Computer Vision and Pattern Recognition WorkshopsSeattle, United StatesIEEEJune 2024, 440-448HALDOI
• 17 inproceedingsF.Felix Kwok and D. N.Djahou N Tognon. A Parallel in Time Algorithm Based on ParaExp for Optimal Control Problems.2024 IEEE 63rd Conference on Decision and Control (CDC)MiCo, Milan, ItalySeptember 2024HAL

Scientific books

• 18 bookJ.Julien Guillod. Programmation Python par la pratique - 2e édition: Problèmes et exercices corrigés.Sciences SupDunodAugust 2024HALback to text
• 19 bookJ.Julien Guillod. Python Programming for Mathematics.The Python SeriesChapman & Hall/CRCJanuary 2025HALDOIback to text

Reports & preprints

• 20 miscJ.Joubine Aghili, J. Z.Joy Zialesi Atokple, M.Marie Billaud-Friess, G.Guillaume Garnier, O.Olga Mula and D. N.Djahou Norbert Tognon. A Dynamical Neural Galerkin Scheme for Filtering Problems.January 2024HAL
• 21 miscC.Chourouk El Hassanieh, S.Samer Israwi, H.Henrik Kalisch, D.Dimitrios Mitsotakis and A.Amutha Senthilkumar. Mechanical balance laws for two-dimensional Boussinesq systems.2024HAL
• 22 miscC.Chourouk El Hassanieh, M.Mathieu Rigal and J.Jacques Sainte-Marie. Implicit kinetic schemes for the Saint-Venant system.2024HAL
• 23 miscE.Emmanuel Franck, I.Ibtissem Lannabi, Y.Youssouf Nasseri, L.Laurent Navoret, G.Giuseppe Parasiliti Rantone and G.Guillaume Steimer. Hyperbolic reduced model for Vlasov-Poisson equation with Fokker-Planck collision.April 2024HAL
• 24 miscP. J.Paguiel Javan Hossie, B.Béatrice Laroche, T.Thibault Malou, L.Lucas Perrin, T.Thomas Saigre and L.Lorenzo Sala. Surrogate modeling of interactions in microbial communities through Physics-Informed Neural Networks..January 2025HAL
• 25 miscL.Long Phan, A.Alice Gatti, Z.Ziwen Han, N.Nathaniel Li, J.Josephina Hu, H.Hugh Zhang, S.Sean Shi, M.Michael Choi, A.Anish Agrawal, A.Arnav Chopra, A.Adam Khoja, R.Ryan Kim, J.Jason Hausenloy, O.Oliver Zhang, M.Mantas Mazeika, D.Daron Anderson, T.Tung Nguyen, M.Mobeen Mahmood, F.Fiona Feng, S. Y.Steven Y. Feng, H.Haoran Zhao, M.Michael Yu, V.Varun Gangal, C.Chelsea Zou, Z.Zihan Wang, J. P.Jessica P. Wang, P.Pawan Kumar, O.Oleksandr Pokutnyi, R.Robert Gerbicz, S.Serguei Popov, J.-C.John-Clark Levin, M.Mstyslav Kazakov, J.Johannes Schmitt, G.Geoff Galgon, A.Alvaro Sanchez, Y.Yongki Lee, W.Will Yeadon, S.Scott Sauers, M.Marc Roth, C.Chidozie Agu, S.Søren Riis, F.Fabian Giska, S.Saiteja Utpala, Z.Zachary Giboney, G. M.Gashaw M. Goshu, J. o.Joan of Arc Xavier, S.-J.Sarah-Jane Crowson, M. M.Mohinder Maheshbhai Naiya, N.Noah Burns, L.Lennart Finke, Z.Zerui Cheng, H.Hyunwoo Park, F.Francesco Fournier-Facio, J.John Wydallis, M.Mark Nandor, A.Ankit Singh, T.Tim Gehrunger, J.Jiaqi Cai, B.Ben Mccarty, D.Darling Duclosel, J.Jungbae Nam, J.Jennifer Zampese, R. G.Ryan G. Hoerr, A.Aras Bacho, G. A.Gautier Abou Loume, A.Abdallah Galal, H.Hangrui Cao, A. C.Alexis C Garretson, D.Damien Sileo, Q.Qiuyu Ren, D.Doru Cojoc, P.Pavel Arkhipov, U.Usman Qazi, L.Lianghui Li, S.Sumeet Motwani, C. S.Christian Schroeder de Witt, E.Edwin Taylor, J.Johannes Veith, E.Eric Singer, T. D.Taylor D. Hartman, P.Paolo Rissone, J.Jaehyeok Jin, J. W.Jack Wei Lun Shi, C. G.Chris G. Willcocks, J.Joshua Robinson, A.Aleksandar Mikov, A.Ameya Prabhu, L.Longke Tang, X.Xavier Alapont, J. L.Justine Leon Uro, K.Kevin Zhou, E. d.Emily de Oliveira Santos, A. P.Andrey Pupasov Maksimov, E.Edward Vendrow, K.Kengo Zenitani, J.Julien Guillod, Y.Yuqi Li, J.Joshua Vendrow, V.Vladyslav Kuchkin, N.Ng Ze-An, P.Pierre Marion, D.Denis Efremov, J.Jayson Lynch, K.Kaiqu Liang, A.Andrew Gritsevskiy, D.Dakotah Martinez, B.Ben Pageler, N.Nick Crispino, D.Dimitri Zvonkine, N. W.Natanael Wildner Fraga, S.Saeed Soori, O.Ori Press, H.Henry Tang, J.Julian Salazar, S. R.Sean R. Green, L.Lina Brüssel, M.Moon Twayana, A.Aymeric Dieuleveut, T. R.T. Ryan Rogers, W.Wenjin Zhang, B.Bikun Li, J.Jinzhou Yang, A.Arun Rao, G.Gabriel Loiseau, M.Mikhail Kalinin, M.Marco Lukas, C.Ciprian Manolescu, S.Subrata Mishra, A. G.Ariel Ghislain Kemogne Kamdoum, T.Tobias Kreiman, T.Tad Hogg, A.Alvin Jin, C.Carlo Bosio, G.Gongbo Sun, B. P.Brian P Coppola, T.Tim Tarver, H.Haline Heidinger, R.Rafael Sayous, S.Stefan Ivanov, J. M.Joseph M Cavanagh, J.Jiawei Shen, J. M.Joseph Marvin Imperial, P.Philippe Schwaller, S.Shaipranesh Senthilkuma, A. M.Andres M Bran, A.Ali Dehghan, A.Andres Algaba, B.Brecht Verbeken, D.David Noever, R.Ragavendran P V, L.Lisa Schut, I.Ilia Sucholutsky, E.Evgenii Zheltonozhskii, D.Derek Lim, R.Richard Stanley, S.Shankar Sivarajan, T.Tong Yang, J.John Maar, J.Julian Wykowski, M.Martí Oller, J.Jennifer Sandlin, A.Anmol Sahu, Y.Yuzheng Hu, S.Sara Fish, N.Nasser Heydari, A.Archimedes Apronti, K.Kaivalya Rawal, T. G.Tobias Garcia Vilchis, Y.Yuexuan Zu, M.Martin Lackner, J.James Koppel, J.Jeremy Nguyen, D. S.Daniil S. Antonenko, S.Steffi Chern, B.Bingchen Zhao, P.Pierrot Arsene, A.Alan Goldfarb, S.Sergey Ivanov, R.Rafał Poświata, C.Chenguang Wang, D.Daofeng Li, D.Donato Crisostomi, A.Andrea Achilleos, B.Benjamin Myklebust, A.Archan Sen, D.David Perrella, N.Nurdin Kaparov, M. H.Mark H Inlow, A.Allen Zang, E.Elliott Thornley, D.Daniil Orel, V.Vladislav Poritski, S.Shalev Ben-David, Z.Zachary Berger, P.Parker Whitfill, M.Michael Foster, D.Daniel Munro, L.Linh Ho, D. B.Dan Bar Hava, A.Aleksey Kuchkin, R.Robert Lauff, D.David Holmes, F.Frank Sommerhage, K.Keith Schneider, Z.Zakayo Kazibwe, N.Nate Stambaugh, M.Mukhwinder Singh, I.Ilias Magoulas, D.Don Clarke, D. H.Dae Hyun Kim, F. M.Felipe Meneguitti Dias, V.Veit Elser, K. P.Kanu Priya Agarwal, V. E.Victor Efren Guadarrama Vilchis, I.Immo Klose, C.Christoph Demian, U.Ujjwala Anantheswaran, A.Adam Zweiger, G.Guglielmo Albani, J.Jeffery Li, N.Nicolas Daans, M.Maksim Radionov, V.Václav Rozhoň, Z.Ziqiao Ma, C.Christian Stump, M.Mohammed Berkani, J.Jacob Platnick, V.Volodymyr Nevirkovets, L.Luke Basler, M.Marco Piccardo, F.Ferenc Jeanplong, N.Niv Cohen, J.Josef Tkadlec, P.Paul Rosu, P.Piotr Padlewski, S.Stanislaw Barzowski, K.Kyle Montgomery, A.Aline Menezes, A.Arkil Patel, Z.Zixuan Wang, J.Jamie Tucker-Foltz, J.Jack Stade, T.Tom Goertzen, F.Fereshteh Kazemi, J.Jeremiah Milbauer, J. A.John Arnold Ambay, A.Abhishek Shukla, Y. C.Yan Carlos Leyva Labrador, A.Alan Givré, H.Hew Wolff, V.Vivien Rossbach, M. F.Muhammad Fayez Aziz, Y.Younesse Kaddar, Y.Yanxu Chen, R.Robin Zhang, J.Jiayi Pan, A.Antonio Terpin, N.Niklas Muennighoff, H.Hailey Schoelkopf, E.Eric Zheng, A.Avishy Carmi, A.Adam Jones, J.Jainam Shah, E. D.Ethan D. L. Brown, K.Kelin Zhu, M.Max Bartolo, R.Richard Wheeler, A.Andrew Ho, S.Shaul Barkan, J.Jiaqi Wang, M.Martin Stehberger, E.Egor Kretov, K.Kaustubh Sridhar, Z.Zienab El-Wasif, A.Anji Zhang, D.Daniel Pyda, J.Joanna Tam, D. M.David M. Cunningham, V.Vladimir Goryachev, D.Demosthenes Patramanis, M.Michael Krause, A.Andrew Redenti, D.Daniel Bugas, D.David Aldous, J.Jesyin Lai, S.Shannon Coleman, M.Mohsen Bahaloo, J.Jiangnan Xu, S.Sangwon Lee, S.Sandy Zhao, N.Ning Tang, M. K.Michael K. Cohen, M.Micah Carroll, O.Orr Paradise, J. H.Jan Hendrik Kirchner, S.Stefan Steinerberger, M.Maksym Ovchynnikov, J. O.Jason O. Matos, A.Adithya Shenoy, B. A.Benedito Alves de Oliveira Junior, M.Michael Wang, Y.Yuzhou Nie, P.Paolo Giordano, P.Philipp Petersen, A.Anna Sztyber-Betley, P.Priti Shukla, J.Jonathan Crozier, A.Antonella Pinto, S.Shreyas Verma, P.Prashant Joshi, Z.-X.Zheng-Xin Yong, A.Allison Tee, J.Jérémy Andréoletti, O.Orion Weller, R.Raghav Singhal, G.Gang Zhang, A.Alexander Ivanov, S.Seri Khoury, H.Hamid Mostaghimi, K.Kunvar Thaman, Q.Qijia Chen, T. Q.Tran Quoc Khánh, J.Jacob Loader, S.Stefano Cavalleri, H.Hannah Szlyk, Z.Zachary Brown, J.Jonathan Roberts, W.William Alley, K.Kunyang Sun, R.Ryan Stendall, M.Max Lamparth, A.Anka Reuel, T.Ting Wang, H.Hanmeng Xu, S. G.Sreenivas Goud Raparthi, P.Pablo Hernández-Cámara, F.Freddie Martin, D.Dmitry Malishev, T.Thomas Preu, T.Tomek Korbak, M.Marcus Abramovitch, D.Dominic Williamson, Z.Ziye Chen, B.Biró Bálint, M. S.M Saiful Bari, P.Peyman Kassani, Z.Zihao Wang, B.Behzad Ansarinejad, L. P.Laxman Prasad Goswami, Y.Yewen Sun, H.Hossam Elgnainy, D.Daniel Tordera, G.George Balabanian, E.Earth Anderson, L.Lynna Kvistad, A. J.Alejandro José Moyano, R.Rajat Maheshwari, A.Ahmad Sakor, M.Murat Eron, I. C.Isaac C. Mcalister, J.Javier Gimenez, I.Innocent Enyekwe, A. F.Andrew Favre D. O., S.Shailesh Shah, X.Xiaoxiang Zhou, F.Firuz Kamalov, R.Ronald Clark, S.Sherwin Abdoli, T.Tim Santens, K.Khalida Meer, H. K.Harrison K Wang, K.Kalyan Ramakrishnan, E.Evan Chen, A.Alessandro Tomasiello, G. B.G. Bruno de Luca, S.-Z.Shi-Zhuo Looi, V.-K.Vinh-Kha Le, N.Noam Kolt, N.Niels Mündler, A.Avi Semler, E.Emma Rodman, J.Jacob Drori, C. J.Carl J Fossum, M.Milind Jagota, R.Ronak Pradeep, H.Honglu Fan, T.Tej Shah, J.Jonathan Eicher, M.Michael Chen, K.Kushal Thaman, W.William Merrill, C.Carter Harris, J.Jason Gross, I.Ilya Gusev, A.Asankhaya Sharma, S.Shashank Agnihotri, P.Pavel Zhelnov, S.Siranut Usawasutsakorn, M.Mohammadreza Mofayezi, S.Sergei Bogdanov, A.Alexander Piperski, M.Marc Carauleanu, D. K.David K. Zhang, D.Dylan Ler, R.Roman Leventov, I.Ignat Soroko, T.Thorben Jansen, P.Pascal Lauer, J.Joshua Duersch, V.Vage Taamazyan, W.Wiktor Morak, W.Wenjie Ma, W.William Held, T. Đ.Tran Đuc Huy, R.Ruicheng Xian, A. R.Armel Randy Zebaze, M.Mohanad Mohamed, J. N.Julian Noah Leser, M. X.Michelle X Yuan, L.Laila Yacar, J.Johannes Lengler, H.Hossein Shahrtash, E.Edson Oliveira, J. W.Joseph W. Jackson, D. E.Daniel Espinosa Gonzalez, A.Andy Zou, M.Muthu Chidambaram, T.Timothy Manik, H.Hector Haffenden, D.Dashiell Stander, A.Ali Dasouqi, A.Alexander Shen, E.Emilien Duc, B.Bita Golshani, D.David Stap, M.Mikalai Uzhou, A. B.Alina Borisovna Zhidkovskaya, L.Lukas Lewark, M.Mátyás Vincze, D.Dustin Wehr, C.Colin Tang, Z.Zaki Hossain, S.Shaun Phillips, J.Jiang Muzhen, F.Fredrik Ekström, A.Angela Hammon, O.Oam Patel, N.Nicolas Remy, F.Faraz Farhidi, G.George Medley, F.Forough Mohammadzadeh, M.Madellene Peñaflor, H.Haile Kassahun, A.Alena Friedrich, C.Claire Sparrow, T.Taom Sakal, O.Omkar Dhamane, A. K.Ali Khajegili Mirabadi, E.Eric Hallman, M.Mike Battaglia, M.Mohammad Maghsoudimehrabani, H.Hieu Hoang, A.Alon Amit, D.Dave Hulbert, R.Roberto Pereira, S.Simon Weber, S.Stephen Mensah, N.Nathan Andre, A.Anton Peristyy, C.Chris Harjadi, H.Himanshu Gupta, S.Stephen Malina, S.Samuel Albanie, W.Will Cai, M.Mustafa Mehkary, F.Frank Reidegeld, A.-K.Anna-Katharina Dick, C.Cary Friday, J.Jasdeep Sidhu, W.Wanyoung Kim, M.Mariana Costa, H.Hubeyb Gurdogan, B.Brian Weber, H.Harsh Kumar, T.Tong Jiang, A.Arunim Agarwal, C.Chiara Ceconello, W. S.Warren S. Vaz, C.Chao Zhuang, H.Haon Park, A. R.Andrew R. Tawfeek, D.Daattavya Aggarwal, M.Michael Kirchhof, L.Linjie Dai, E.Evan Kim, J.Johan Ferret, Y.Yuzhou Wang, M.Minghao Yan, K.Krzysztof Burdzy, L.Lixin Zhang, A.Antonio Franca, D. T.Diana T. Pham, K. Y.Kang Yong Loh, J.Joshua Robinson, S.Shreen Gul, G.Gunjan Chhablani, Z.Zhehang Du, A.Adrian Cosma, C.Colin White, R.Robin Riblet, P.Prajvi Saxena, J.Jacob Votava, V.Vladimir Vinnikov, E.Ethan Delaney, S.Shiv Halasyamani, S. M.Syed M. Shahid, J.-C.Jean-Christophe Mourrat, L.Lavr Vetoshkin, R.Renas Bacho, V.Vincent Ginis, A.Aleksandr Maksapetyan, F.Florencia de la Rosa, X.Xiuyu Li, G.Guillaume Malod, L.Leon Lang, J.Julien Laurendeau, F.Fatimah Adesanya, J.Julien Portier, L.Lawrence Hollom, V.Victor Souza, Y. A.Yuchen Anna Zhou, Y.Yiğit Yalın, G. D.Gbenga Daniel Obikoya, L.Luca Arnaboldi, F.Filippo Bigi, K.Kaniuar Bacho, P.Pierre Clavier, G.Gabriel Recchia, M.Mara Popescu, N.Nikita Shulga, N. M.Ngefor Mildred Tanwie, T. C.Thomas C. H. Lux, B.Ben Rank, C.Colin Ni, A.Alesia Yakimchyk, H.Huanxu Liu, O.Olle Häggström, E.Emil Verkama, H.Himanshu Narayan, H.Hans Gundlach, L.Leonor Brito-Santana, B.Brian Amaro, V.Vivek Vajipey, R.Rynaa Grover, Y.Yiyang Fan, G. P.Gabriel Poesia Reis E Silva, L.Linwei Xin, Y.Yosi Kratish, J.Jakub Łucki, W.-D.Wen-Ding Li, J.Justin Xu, K. J.Kevin Joseph Scaria, F.Freddie Vargus, F.Farzad Habibi, E.Emanuele Rodolà, J.Jules Robins, V.Vincent Cheng, D.Declan Grabb, I.Ida Bosio, T.Tony Fruhauff, I.Ido Akov, E. J.Eve J. Y. Lo, H.Hao Qi, X.Xi Jiang, B.Ben Segev, J.Jingxuan Fan, S.Sarah Martinson, E. Y.Erik Y. Wang, K.Kaylie Hausknecht, M. P.Michael P. Brenner, M.Mao Mao, Y.Yibo Jiang, X.Xinyu Zhang, D.David Avagian, E. J.Eshawn Jessica Scipio, M. R.Muhammad Rehan Siddiqi, A.Alon Ragoler, J.Justin Tan, D.Deepakkumar Patil, R.Rebeka Plecnik, A.Aaron Kirtland, R. G.Roselynn Grace Montecillo, S.Stephane Durand, O. F.Omer Faruk Bodur, Z.Zahra Adoul, M.Mohamed Zekry, G.Guillaume Douville, A.Ali Karakoc, T. C.Tania C. B. Santos, S.Samir Shamseldeen, L.Loukmane Karim, A.Anna Liakhovitskaia, N.Nate Resman, N.Nicholas Farina, J. C.Juan Carlos Gonzalez, G.Gabe Maayan, S.Sarah Hoback, R. d.Rodrigo de Oliveira Pena, G.Glen Sherman, H.Hodjat Mariji, R.Rasoul Pouriamanesh, W.Wentao Wu, G.Gözdenur Demir, S.Sandra Mendoza, I.Ismail Alarab, J.Joshua Cole, D.Danyelle Ferreira, B.Bryan Johnson, H.Hsiaoyun Milliron, M.Mohammad Safdari, L.Liangti Dai, S.Siriphan Arthornthurasuk, A.Alexey Pronin, J.Jing Fan, A.Angel Ramirez-Trinidad, A.Ashley Cartwright, D.Daphiny Pottmaier, O.Omid Taheri, D.David Outevsky, S.Stanley Stepanic, S.Samuel Perry, L.Luke Askew, R. A.Raúl Adrián Huerta Rodríguez, A.Abdelkader Dendane, S.Sam Ali, R.Ricardo Lorena, K.Krishnamurthy Iyer, S. M.Sk Md Salauddin, M.Murat Islam, J.Juan Gonzalez, J.Josh Ducey, R.Russell Campbell, M.Maja Somrak, V.Vasilios Mavroudis, E.Eric Vergo, J.Juehang Qin, B.Benjámin Borbás, E.Eric Chu, J.Jack Lindsey, A.Anil Radhakrishnan, A.Antoine Jallon, I. M.I. M. J. Mcinnis, A.Alex Hoover, S.Sören Möller, S.Song Bian, J.John Lai, T.Tejal Patwardhan, S.Summer Yue, A.Alexandr Wang and D.Dan Hendrycks. Humanity's Last Exam.January 2025HAL
• 26 misc C.Claire Rogel Gaillard and J.Jacques Sainte-Marie. Agroecology and digital technology: what synergy for the ecological transition? October 2024 HAL
• 27 miscF.Francesco Tucciarone, L.Long Li, E.Etienne Mémin and L.Louis Thiry. Transport noise defined from wavelet transform for model-based stochastic ocean models.2024HAL

Software

• 28 softwareO.Olivier Delestre, C.Carine Lucas, P.-A.Pierre-Antoine Ksinant Garcia, F.Frédéric Darboux, C.Christian Laguerre, N.Noémie Gaveau, M.Maxime Rougier, A.-C.Anne-Céline Boulanger, M.Marco Mancini, S.Stéphane Cordier and F.François James. SWASHES: Shallow Water Analytic Solutions for Hydraulic and Environmental Studies.1.04.01January 2024Université d'Orléans (UO), Orléans, FRA.; INRA Institut National de la Recherche Agronomique; CNRS - Centre National de la Recherche Scientifique lic: CeCILL Free Software License Agreement v2.0.HALSoftware HeritageVCS