RESEARCH CENTRE

Inria Centre
at Rennes University

IN PARTNERSHIP WITH:

CNRS, Université de Rennes

2023
ACTIVITY REPORT

Project-Team

SIMSMART

SIMulating Stochastic Models with
pARTicles

IN COLLABORATION WITH: Institut de recherche mathématique de
Rennes (IRMAR)

DOMAIN

Applied Mathematics, Computation and
Simulation

THEME

Stochastic approaches

Contents

Project-Team SIMSMART

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

4 Application domains

4.1 Domain 1 – Computational Physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Domain 2 – Meteorology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Other Applicative Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 New software, platforms, open data

7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 Screening4L0Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.2 Screen&Relax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.3 npSEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.4 NHMSAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.5 3D Winds Fields Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.6 Screening4SLOPE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8 New results

8.1 Objective 1 – Monte Carlo simulation and Stochastic analysis . . . . . . . . . . . . . . . . . .
8.2 Objective 2 and 3 – Data assimilation and statistics . . . . . . . . . . . . . . . . . . . . . . . .
8.3 Objective 4 – Model Reduction and Sparsity . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.1.1 CIFRE grants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.1.2 Meteorological Satellite Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . .

10 Partnerships and cooperations

10.1 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.1.1 ANR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2 Local initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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11 Dissemination

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11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.1.2 Journal
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.1.3 Main talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
11.1.4 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 10
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
11.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
11.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

12 Scientific production

12.1 Publications of the year

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Project SIMSMART

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Project-Team SIMSMART

Creation of the Project-Team: 2019 January 01

Keywords

Computer sciences and digital sciences

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. – Computation-data 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

Other research topics and application domains

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

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Mathias Rousset [Team leader, INRIA, Researcher, HDR]

• Frédéric Cérou [INRIA, Researcher]

• Cédric Herzet [INRIA, Researcher, until Aug 2023, HDR]

• Patrick Héas [INRIA, Researcher]

Faculty Member

• Valérie Monbet [UNIV RENNES, Professor, HDR]

PhD Students

• François Ernoult [UNIV RENNES, until Sep 2023]

• Theo Guyard [INSA RENNES]

• Thu Le Tran [UNIV RENNES, until Sep 2023]

Interns and Apprentices

• Chloé Caville [CENTRALE NANTES, Intern, from Apr 2023 until Aug 2023]

Administrative Assistant

• Gunther Tessier [INRIA]

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 simula-
tion 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 multi-scales. It also results in much less control over simulation uncertainty.

Within this highly challenging context, SIMSMART positions itself as a mathematical and compu-
tational 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, bottom-up 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.

Project SIMSMART

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Second, the chaotic dynamical behaviors of the systems at stake, coupled with such multi-scale 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 pseudo-random 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 non-superficial 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 challen-
ging world of complex (e.g. high dimensional and/or multi-scale), 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: Non-parametric 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. Object-
ive 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 non-linear particle filtering, where linear data assimilation methods remain the
state-of-the-art approaches.

The increasing size of recorded observational data and the increasing complexity of models also sug-
gest to devote more effort into non-parametric 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 dimen-
sional) dynamical systems depending on uncertain parameters, the construction of relevant reduced-
order models becomes a key topic. Model reduction aims at proposing efficient algorithmic procedures
for the resolution (to some reasonable accuracy) of high-dimensional systems of parametric equations.
This overall objective entails many different subtasks:1) the identification of low-dimensional surrog-
ates of the target “solution” manifold, 2) The devise of efficient methodologies of resolution exploiting
low-dimensional 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+Object-

ive 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 simu-
lating on large, macroscopic random times, and simulating rare events (see Objective 1). The link between

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Inria Annual Report 2023

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 speed-up compar-
ative 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 non-parametric statistical
inference (see Objective 3).

Hindsight suggests that all the latter challenges are related. Indeed, the contemporary digital condi-
tion, 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 large-scale 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 bio-chemistry, 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 project-teams.

SIMSMART also focuses on various models described by partial differential equations (reaction-

diffusion, 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 di-
mensional prior models, and observations in large dimension. The use of some linear assumption in
prior models (Kalman filtering) to filter non-linear hydrodynamical phenomena is the state-of-the-art
approach, and a current field of research, but is plagued with intractable instabilities.

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This context motivates two research trends: (i) the introduction of non-parametric, model-free prior
dynamics constructed from a large amount of past, recorded real weather data; and (ii) the development
of appropriate non-linear filtering approaches (Objective 2 and Objective 3).

SIMSMART will also test its new methods on multi-source data collected in North-Atlantic paying

particular attention to coastal areas (e.g. within the inter-Labex SEACS).

4.3 Other Applicative Domains

SIMSMART also focuses on other applications including:

• Tracking and hidden Markov models.

• Robustness and certification in Machine Learning.

5 Social and environmental responsibility

5.1 Footprint of research activities

Members of SimSmart have avoided air traveling, with the notable exception of rare international confer-
ences with publications for PhD students (this year Theo Guyard) which are considered important for
their academic future.

6 Highlights of the year

6.1 Awards

Théo Guyard, "Best student paper award", conference ROADEF 2023.

7 New software, platforms, open data

7.1 New software

7.1.1 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 pseudo-
norm.

URL: https://gitlab.insa-rennes.fr/Theo.Guyard/bnb-screening

Publication: hal-03462171

Contact: Cedric Herzet

Participants: Clément Elvira, Theo Guyard, Cedric Herzet

7.1.2 Screen&Relax

Keywords: Optimization, Sparsity

Functional Description: This software provides optimization routines to efficiently solve the "Elast-

icNet" problem.

URL: https://gitlab.insa-rennes.fr/Theo.Guyard/screen-and-relax

Publication: hal-03462191

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Inria Annual Report 2023

Contact: Cedric Herzet

Participants: Clément Elvira, Theo Guyard, Cedric Herzet

7.1.3 npSEM

Name: Stochastic expectation-maximization algorithm for non-parametric state-space models

Keyword: Statistic analysis

Functional Description: npSEM is the combination of a non-parametric estimate of the dynamic us-
ing local linear regression (LLR), a conditional particle smoother and a stochastic Expectation-
Maximization (SEM) algorithm. Further details of its construction and implementation are intro-
duced in the article An algorithm for non-parametric estimation in state-space models of authors
"T.T.T. Chau, P. Ailliot, V. Monbet", https://doi.org/10.1016/j.csda.2020.107062.

URL: https://github.com/tchau218/npSEM

Contact: Thi Tuyet Trang Chau

Participants: Valérie Monbet, Thi Tuyet Trang Chau

7.1.4 NHMSAR

Name: Non-Homogeneous 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 imple-
mented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the
package handle missing values.

URL: https://CRAN.R-project.org/package=NHMSAR

Contact: Valérie Monbet

Participant: Valérie Monbet

7.1.5 3D Winds Fields Profiles

Functional Description: The algorithm computes 3D Atmospheric Motion Vectors (AMVs) vertical pro-
files, 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: https://www.esa.int/Applications/Observing_the_Earth/Meteorological_mission

s/MetOp/About_IASI

Contact: Patrick Heas

Participant: Patrick Heas

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7.1.6 Screening4SLOPE

Keyword: Optimization

Functional Description: This software provides optimization routines to solve the SLOPE problem by

exploiting "safe screening" reduction techniques.

URL: https://gitlab-research.centralesupelec.fr/2020elvirac/slope-screening

Publication: hal-03400322

Contact: Cedric Herzet

Participants: Clément Elvira, Cedric Herzet

8 New results

8.1 Objective 1 – Monte Carlo simulation and Stochastic analysis

Monte-Carlo simulation

Participants:

Frédéric Cérou, Patrick Héas, Mathias Rousset, François Ernoult.

In [4] we study a real world high dimensional Bayesian sampling problem (weather variables observed
by space imagery) using kinetic Langevin diffusions (Hamiltonian Monte Carlo), and show empirically the
advantage for convergence of an artificial “cold” tempering taming the non-linearities of the likelihood.
In [12], we obtained a rigorous general theorem for strong noise homogeneisation, a recent problem
where a strong (fast) noise coerce a Markov process near an effective sub-state space. Applications include
quantum control and filtering.

In [11], we proposed a new rare event sampling methodology in a context where evaluations of the
score function defining the rare event is amenable to reduced modeling with pointwise error bounds. The
novelty is the use of an Importance Sampling cost criteria that automatically choose the level at which
costly evaluation of the true model are performed.

In [10], we quantify the robustness of a trained network to input uncertainties with a splitting rare

event simulation Monte Carlo method that uses gradient-informed (Langevin-like) kernel.

8.2 Objective 2 and 3 – Data assimilation and statistics

Participants:

Patrick Héas, Valérie Monbet.

In [3], we present an efficient optic flow algorithm for the extraction of vertically resolved 3D atmo-
spheric motion vector (AMV) fields from incomplete hyperspectral image data measures by infrared
sounders.

In [1], a hidden latent Markovian model with parametric noise and non-parametric drift is inferred
from an historical catalog of data (a time series) using a stochastic Expectation-Maximisation/Estimation
iterative scheme (with iteration index k). In the latter, the smoothed (conditional on data) distribution of
the step k hidden model is simulated using advanced pathwise sequential Monte Carlo particle filters
(Conditional Particle Filters with Backward simulation). The step k + 1 model is then estimated using
both parametric maximum likelihood and non-parametric / machine learning tools.

Motivated by applications to weather/climate data, estimation methods based on Gaussian mixtures

for calibration of ensemble forecasts have been performed in [5].

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Inria Annual Report 2023

8.3 Objective 4 – Model Reduction and Sparsity

Participants:

Patrick Héas, Cédric Herzet, Théo Guyard.

In [2], 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", well-studied in the
literature for group-separable sparsity-inducing norms, and aims at identifying the zeros in the solution
of SLOPE.

In [9], [8], [6], [13], we introduce a new methodology dubbed “safe peeling” to accelerate the resolution
of L0-regularized least-squares problems via a Branch-and-Bound (BnB) algorithm. Our procedure
enables to tighten the convex relaxation considered at each node of the BnB decision tree and therefore
potentially allows for more aggressive pruning.

In the context of model reduction, an issue is to find fast algorithms to project onto low-dimensional,
sparse models. [14] studies non-linear approximation of high-dimensional dynamical systems using
low-rank dynamic mode decomposition and embedding of trajectories in a reproducing kernel Hilbert
space (RKHS)

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

9.1.1 CIFRE grants

• PhD project of Victor Bertret: AI and stochastic control for automatic optimal driving of industrial

systems with company Purecontrol.

Participants:

Valérie Monbet.

9.1.2 Meteorological Satellite Data Processing

Participants:

Patrick Héas.

Industrial Partner: EUMETSAT of Darmstadt.

Partner Contact: Regis.Borde@eumetsat.int

The transferred technology concerns an algorithm for the operational and real-time production of
vertically resolved 3D atmospheric motion vector fields (AMVs) from measurements of new hyperspectral
instruments: the infrared radiosounders on the third generation Meteosat satellites (MTG), developed
by the European Space Agency (ESA) and the Infrared Atmospheric Sounding Interferometer (IASI) on
MetOp-A and MetOp-B developed by the French Space Agency (CNES).

10 Partnerships and cooperations

10.1 National initiatives

10.1.1 ANR

ANR MELODY(2020-2024)

Project SIMSMART

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Participants: Cédric Herzet.

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).

The partners involved in the project were: IMT Atlantique (PI: Ronan Fablet), Inria-Rennes,
Inria-Grenoble, Laboratoire d’Océanographie Physique et Spatiale, Institut des géosciences et
de l’environnement, Institut Pierre-Simon Laplace.

ANR SINEQ (2021-2025).

Participants: Mathias Rousset, Frédéric Cérou.

Simulating non-equilibrium stochastic dynamics. The goal of the SINEQ project is, within a
mathematical perspective, to extend various variance reduction techniques used in the Monte
Carlo computation of equilibrium properties of statistical physics models.

The partners involved in the project are: CERMICS (PI: G. Stoltz), CEREMADE and Inria Rennes.

10.2 Local initiatives

Defi interdisciplinaire de l’Université de Rennes 1 (10kE): Caractérisation du microbiote intestinal de
l’individu sédentaire et inactif à l’athlète de haut niveau. Une approche innovante pour prédire le
métabolisme énergétique de l’organisme en réponse à l’exercice ?, avec Frédéric DERBRE (Laboratoire
M2S / Université Rennes 2) .

11 Dissemination

Participants:

Frédéric Cérou, , Théo Guyard, , Patrick Héas, , Cédric Herzet, ,
Valérie Monbet,, Mathias Rousset.

11.1 Promoting scientific activities

Participants:

Frédéric Cérou, , Théo Guyard, , Patrick Héas, , Cédric Herzet, ,
Valérie Monbet,, Mathias Rousset.

11.1.1 Scientific events: organisation

• Frédéric Cérou is organizing the weekly Probability Seminar at IRMAR, Univ Rennes.

• Valérie Monbet: Co-organisation du workshop Roscoff, 13-15 nov 2023 "Data Science pour les

risques côtiers".

11.1.2 Journal

Reviewer - reviewing activities Many journals including top journals in applied mathematics, probabil-
ity, and statistics.

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Inria Annual Report 2023

11.1.3 Main talks

• Mathias Rousset, Monte Carlo methods and Applications.

• Théo Guyard PGMODAYS 2023, with contribution [7]

• Théo Guyard, SIAM Conference on Optimization - Seattle, USA.

• Cédric Herzet, JSTAR.

• Valérie Monbet, ECMI 2023, Pologne.

11.1.4 Leadership within the scientific community

Valérie Monbet is:

• Co-directrice de l’Agence Lebesgue depuis janvier 2023

• Membre du bureau d’AMIES.

• Membre du CNU section 26 jusqu’en dec. 2023.

11.2 Teaching - Supervision - Juries

Participants:

Patrick Héas, , Cédric Herzet, , Valérie Monbet,, Mathias Rousset.

11.2.1 Teaching

Cédric Herzet has given:

• Traitement du signal, ENS Rennes, Master 1 : Responsable du module, 10h.

• Apprentissage, ENSAI Rennes, Master 2 : 9h.

• Régularisation, ENSAI, Master 2 : Responsable du module, 18h.

Mathias Rousset has given

• A course at summer school of ANR SINEQ.

• préparation à l’agrégation, modélistaion optino proba-stat.

Patrick Héas has given

• A course on Algorithmique et Complexité , Ecole supérieure d’ingénieurs de Rennes (ESIR), 2-ième

année, université de Rennes 1.

11.2.2 Supervision

PhD Supervision and Defense

M. Rousset has been supervising the PhD of: Karim Tit: Rare event analysis of the Reliability of Deep

Neural Networks, CIFRE Inria and Thalès, starting Jan. 2021, co-supervision: T.Furon.

M. Rousset has been supervising the PhD of François Ernoult, Small noise analysis of rare event splitting
algorithms, UR1 and Région Bretagne, starting Sept. 2020. The PhD has been defended december
15 (HAL publication will be available next year).

C. Herzet has been supervising the PhD of Théo Guyard, Screening methods for non-convex sparse

representations. INSA, starting Sept. 2021, co-supervision James Ledoux.

Project SIMSMART

11

C. Herzet has been supervising the PhD of Thu Le Tran, Sparse representations in continuous dictionaries.

Application to spectroscopy data. UR1, sarting Sept. 2020, co-supervision: Valérie Monbet.

V. Monbet have been supervising the CIFRE PhD of .Victor Bertret with company Purecontrol (Rennes),
Apprentissage machine et contrôle stochastique pour un pilotage automatique optimisé de systèmes
industriels. Starting sept. 2023.

Other Valérie Monbet a encadré le PostDoc (financé par l’Agence Lebesgue) de Pierre Houedry. Thème :
Machine Learning et Deep Learning pour la détection et la classification de pollen dans l’air.

11.2.3 Juries

Valérie Monbet has been part of the following juries:

• HDR Pierre Tandeo (IMT atlantique, Brest)

• HDR Matthieu Marbac (ENSAI)

• HDR Audrey Lavenu (UR1)

• PhD Benjamin Dufée (INRIA)

Cédric Herzet has been part of the jury:

• PhD Gilles Monnoyer (UCLouvain, Belgique)

11.3 Popularization

Participants:

Patrick Héas.

Patrick Héas: intervention au lycée Frédéric OZANAM dans une classe de seconde pour la présentation

de l’activité d’un chercheur en mathématiques appliquées.

12 Scientific production

12.1 Publications of the year

International journals

[1] T. T. T. Chau, P. Ailliot, V. Monbet and P. Tandeo. ‘Comparison of simulation-based algorithms for
parameter estimation and state reconstruction in nonlinear state-space models’. In: Discrete and
Continuous Dynamical Systems - Series S 16.2 (2023), pp. 240–264. DOI: 10.3934/dcdss.2022054.
URL: https://imt-atlantique.hal.science/hal-03616079.

[2] C. Elvira and C. Herzet. ‘Safe rules for the identification of zeros in the solution of the SLOPE
problem’. In: SIAM Journal on Mathematics of Data Science 5.1 (2023), pp. 147–173. DOI: 10.1137
/21M1457631. URL: https://hal.science/hal-03400322.

[3] P. Héas, O. Hautecoeur and R. Borde. ‘3D wind field profiles from hyperspectral sounders: revisiting
optic-flow from a meteorological perspective’. In: Physica Scripta 98.11 (3rd Oct. 2023), p. 115208.
DOI: 10.1088/1402-4896/acf3a8. URL: https://inria.hal.science/hal-04397285.

[4] P. Héas, F. Cérou and M. Rousset. ‘Chilled sampling for uncertainty quantification: a motivation
from a meteorological inverse problem’. In: Inverse Problems 98.11 (11th Dec. 2023), p. 115208. DOI:
10.1088/1361-6420/ad141f. URL: https://inria.hal.science/hal-04354086.

[5] G. Jouan, A. Cuzol, V. Monbet and G. Monnier. ‘Gaussian mixture models for clustering and calibra-
tion of ensemble weather forecasts’. In: Discrete and Continuous Dynamical Systems - Series S 16.2
(2023), pp. 309–328. DOI: 10.3934/dcdss.2022037. URL: https://hal.science/hal-0361936
4.

12

Inria Annual Report 2023

International peer-reviewed conferences

[6] T. Guyard. ‘Un solveur efficace pour la résolution de problèmes parcimonieux avec pénalité L0’. In:
ROADEF 2023 - 24ème édition du congrès annuel de la Société Française de Recherche Opération-
nelle et d’Aide à la Décision. Rennes, France, 25th Nov. 2022, pp. 1–6. URL: https://hal.science
/hal-03960204.

[7] T. Guyard, C. Herzet and C. Elvira. ‘Screen & Relax for Sparse Support Identification’. In: PGMO
DAYS 2023 - Conférence annuelle du Programme Gaspard Monge pour l’Optimisation. Paris Saclay,
France, 2023, pp. 1–1. URL: https://hal.science/hal-04313014.

[8] T. Guyard, G. Monnoyer, C. Elvira and C. Herzet. ‘Safe peeling for L0-regularized least-squares’. In:
EUSIPCO 2023 - 31st European Signal Processing Conference. Helsinki, Finland: IEEE, 4th Sept.
2023, pp. 1–5. DOI: 10.23919/EUSIPCO58844.2023.10290041. URL: https://hal.science/ha
l-04271074.

[9] T. Guyard, G. Monnoyer, C. Elvira and C. Herzet. ‘Peeling pour le problème des moindres carrés avec
régularisation L0’. In: GRETSI 2023 - XXIXème Colloque Francophone de Traitement du Signal et des
Images. Grenoble, France, 28th Aug. 2023, pp. 1–4. URL: https://hal.science/hal-04271083.

[10] K. Tit, T. Furon and M. Rousset. ‘Gradient-Informed Neural Network Statistical Robustness Estima-
tion’. In: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics
(AISTATS). AISTATS 2023 - 26th International Conference on Artificial Intelligence and Statistics.
Vol. 206. Valencia, Spain, Apr. 2023. URL: https://inria.hal.science/hal-03987284.

Reports & preprints

[11] F. Cérou, P. Héas and M. Rousset. Adaptive Reduced Multilevel Splitting. 23rd Dec. 2023. URL:

https://inria.hal.science/hal-04364553.

[12] D. Faure and M. Rousset. A general martingale approach to large noise homogenization. 24th Dec.
2023. DOI: 10.48550/arXiv.2304.09624. URL: https://hal.science/hal-04363393.

[13] T. Guyard, G. Monnoyer, C. Herzet and C. Elvira. Safe Peeling for L0-Regularized Least-Squares with

supplementary material. 6th June 2023. URL: https://hal.science/hal-04118979.

[14] P. Héas, C. Herzet and B. Combès. Non-Linear Reduced Modeling by Generalized Kernel-Based

Dynamic Mode Decomposition. 19th Dec. 2023. URL: https://hal.science/hal-04354110.
<scratchpad>
- Main topic: Research Centre SIMSMART (Inria Centre at Rennes University)
- Key points:
  - Focus on stochastic modeling and Monte Carlo simulation methods
  - Applications in computational physics, meteorology, and other domains
  - Emphasis on rare event simulation, data assimilation, and model reduction
  - Collaboration with other research teams and industry partners
- Structure: Report format with various sections (Project-Team, Activity Report, etc.)
- Potential artifacts to ignore: Links, HTML tags, and web-related elements
</scratchpad>

After analyzing the document, I have identified the following key points:

- SIMSMART is a research centre focused on stochastic modeling and Monte Carlo simulation methods.
- The centre has applications in computational physics, meteorology, and other domains, including tracking and hidden Markov models, robustness and certification in Machine Learning, and non-parametric statistical inference.
- SIMSMART aims to disrupt the classical field of particle Monte Carlo simulation by creating deeper mathematical frameworks adapted to the management of contemporary highly sophisticated physical models.
- The centre has collaborations with other research teams and industry partners, including EUMETSAT and Purecontrol.
- SIMSMART has achieved notable results, including the development of new software and platforms, such as Screening4L0Problem and Screen&Relax.
- The centre has also published several papers in top journals and conferences, including Discrete and Continuous Dynamical Systems, SIAM Journal on Mathematics of Data Science, and EUSIPCO.

<final_summary>
SIMSMART is a research centre focused on stochastic modeling and Monte Carlo simulation methods, with applications in computational physics, meteorology, and other domains. The centre aims to disrupt the classical field of particle Monte Carlo simulation by creating deeper mathematical frameworks adapted to the management of contemporary highly sophisticated physical models. SIMSMART has collaborations with other research teams and industry partners, including EUMETSAT and Purecontrol, and has achieved notable results in the development of new software and platforms, as well as publications in top journals and conferences.
SIMSMART is a research centre focused on stochastic modeling and Monte Carlo simulation methods, with applications in computational physics, meteorology, and other domains. The centre aims to disrupt the classical field of particle Monte Carlo simulation by creating deeper mathematical frameworks adapted to the management of contemporary highly sophisticated physical models. SIMSMART has collaborations with other research teams and industry partners, including EUMETSAT and Purecontrol, and has achieved notable results in the development of new software and platforms, as well as publications in top journals and conferences.