Publications

2022

• Anytime Performance Assessment in Blackbox Optimization Benchmarking
  
  • Hansen Nikolaus
  • Auger Anne
  • Brockhoff Dimo
  • Tusar Tea
  IEEE Transactions on Evolutionary Computation, Institute of Electrical and Electronics Engineers, 2022, 26 (6), pp.1293--1305. We present concepts and recipes for the anytime performance assessment when benchmarking optimization algorithms in a blackbox scenario. We consider runtime-oftentimes measured in number of blackbox evaluations needed to reach a target quality-to be a universally measurable cost for solving a problem. Starting from the graph that depicts the solution quality versus runtime, we argue that runtime is the only performance measure with a generic, meaningful, and quantitative interpretation. Hence, our assessment is solely based on runtime measurements. We discuss proper choices for solution quality indicators in single-and multiobjective optimization, as well as in the presence of noise and constraints. We also discuss the choice of the target values, budget-based targets, and the aggregation of runtimes by using simulated restarts, averages, and empirical cumulative distributions which generalize convergence graphs of single runs. The presented performance assessment is to a large extent implemented in the comparing continuous optimizers (COCO) platform freely available at https://github.com/numbbo/coco. (10.1109/TEVC.2022.3210897)
  DOI : 10.1109/TEVC.2022.3210897
• Risk-Averse Stochastic Programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application
  
  • Lima Ricardo
  • Conejo Antonio
  • Giraldi Loïc
  • Le Maitre Olivier
  • Hoteit Ibrahim
  • Knio Omar
  INFORMS Journal on Computing, Institute for Operations Research and the Management Sciences (INFORMS), 2022. This paper compares risk-averse optimization methods to address the self-scheduling and market involvement of a virtual power plant (VPP). The decision-making problem of the VPP involves uncertainty in the wind speed and electricity price forecast. We focus on two methods: risk-averse two-stage stochastic programming (SP) and two-stage adaptive robust optimization (ARO). We investigate both methods concerning formulations, uncertainty and risk, decomposition algorithms, and their computational performance. To quantify the risk in SP, we use the conditional value at risk (CVaR) because it can resemble a worst-case measure, which naturally links to ARO. We use two efficient implementations of the decomposition algorithms for SP and ARO; we assess (1) the operational results regarding first-stage decision variables, estimate of expected profit, and estimate of the CVaR of the profit and (2) their performance taking into consideration different sample sizes and risk management parameters. The results show that similar first-stage solutions are obtained depending on the risk parameterizations used in each formulation. Computationally, we identified three cases: (1) SP with a sample of 500 elements is competitive with ARO; (2) SP performance degrades comparing to the first case and ARO fails to converge in four out of five risk parameters; (3) SP fails to converge, whereas ARO converges in three out of five risk parameters. Overall, these performance cases depend on the combined effect of deterministic and uncertain data and risk parameters. Summary of Contribution: The work presented in this manuscript is at the intersection of operations research and computer science, which are intrinsically related with the scope and mission of IJOC. From the operations research perspective, two methodologies for optimization under uncertainty are studied: risk-averse stochastic programming and adaptive robust optimization. These methodologies are illustrated using an energy scheduling problem. The study includes a comparison from the point of view of uncertainty modeling, formulations, decomposition methods, and analysis of solutions. From the computer science perspective, a careful implementation of decomposition methods using parallelization techniques and a sample average approximation methodology was done . A detailed comparison of the computational performance of both methods is performed. Finally, the conclusions allow establishing links between two alternative methodologies in operations research: stochastic programming and robust optimization. (10.1287/ijoc.2022.1157)
  DOI : 10.1287/ijoc.2022.1157
• Non-linear boundary condition for non-ideal electrokinetic equations in porous media
  
  • Allaire Grégoire
  • Brizzi Robert
  • Labbez Christophe
  • Mikelić Andro
  Applicable Analysis, Taylor & Francis, 2022, 101 (12), pp.4203-4234. This paper studies the partial differential equation describing the charge distribution of an electrolyte in a porous medium. Realistic non-ideal effects are incorporated through the mean spherical approximation (MSA) model which takes into account finite size ions and screening effects. The main novelty is the consideration of a non-constant surface charge density on the pore walls. Indeed, a chemical equilibrium reaction is considered on the boundary to represent the dissociation of ionizable sites on the solid walls. The surface charge density is thus given as a non-linear function of the electrostatic potential. Even in the ideal case, the resulting system is a new variant of the famous Poisson-Boltzmann equation, which still has a monotone structure under quantitative assumptions on the physical parameters. In the non-ideal case, the MSA model brings in additional non-linearities which break down the monotone structure of the system. We prove existence, and sometimes uniqueness, of the solution. Some numerical experiments are performed in 2-d to compare this model with that for a constant surface charge. (10.1080/00036811.2022.2080672)
  DOI : 10.1080/00036811.2022.2080672
• Multidimensional fully adaptive lattice Boltzmann methods with error control based on multiresolution analysis
  
  • Bellotti Thomas
  • Gouarin Loïc
  • Graille Benjamin
  • Massot Marc
  Journal of Computational Physics, Elsevier, 2022, 471, pp.111670. Lattice-Boltzmann methods are known for their simplicity, efficiency and ease of parallelization, usually relying on uniform Cartesian meshes with a strong bond between spatial and temporal discretization. This fact complicates the crucial issue of reducing the computational cost and the memory impact by automatically coarsening the grid where a fine mesh is unnecessary, still ensuring the overall quality of the numerical solution through error control. This work provides a possible answer to this interesting question, by connecting, for the first time, the field of lattice-Boltzmann Methods (LBM) to the adaptive multiresolution (MR) approach based on wavelets. To this end, we employ a MR multi-scale transform to adapt the mesh as the solution evolves in time according to its local regularity. The collision phase is not affected due to its inherent local nature and because we do not modify the speed of the sound, contrarily to most of the LBM/Adaptive Mesh Refinement (AMR) strategies proposed in the literature, thus preserving the original structure of any LBM scheme. Besides, an original use of the MR allows the scheme to resolve the proper physics by efficiently controlling the accuracy of the transport phase. We carefully test our method to conclude on its adaptability to a wide family of existing lattice Boltzmann schemes, treating both hyperbolic and parabolic systems of equations, thus being less problem-dependent than the AMR approaches, which have a hard time guaranteeing an effective control on the error. The ability of the method to yield a very efficient compression rate and thus a computational cost reduction for solutions involving localized structures with loss of regularity is also shown, while guaranteeing a precise control on the approximation error introduced by the spatial adaptation of the grid. The numerical strategy is implemented on a specific open-source platform called SAMURAI with a dedicated data-structure relying on set algebra. (10.1016/j.jcp.2022.111670)
  DOI : 10.1016/j.jcp.2022.111670
• Optimal Electricity Demand Response Contracting with Responsiveness Incentives
  
  • Aïd René
  • Possamaï Dylan
  • Touzi Nizar
  Mathematics of Operations Research, INFORMS, 2022. Demand response programs in retail electricity markets are very popular. However, despite their success in reducing average consumption, the random responsiveness of consumers to price events makes their efficiency questionable to achieve the flexibility needed for electric systems with a large share of renewable energy. This paper aims at designing demand response contracts that allow to act on both the average consumption and its variance. The interaction between a risk-averse producer and a risk-averse consumer is modelled as a principal–agent problem, thus accounting for the moral hazard underlying demand response contracts. The producer, facing the limited flexibility of production, pays an appropriate incentive compensation to encourage the consumer to reduce his average consumption and to enhance his responsiveness. We provide a closed-form solution for the optimal contract in the linear case. We show that the optimal contract has a rebate form where the initial condition of the consumption serves as a baseline and where the consumer is charged a price for energy and a price for volatility. The first-best price for energy is a convex combination of the marginal cost and the marginal value of energy, where the weights are given by the risk-aversion ratios, and the first-best price for volatility is the risk-aversion ratio times the marginal cost of volatility. The second-best price, for energy and volatility, is a decreasing nonlinear function of time inducing decreasing effort. The price for energy is lower (respectively, higher) than the marginal cost of energy during peak-load (respectively, off-peak) periods. We illustrate the potential benefits issued from the implementation of an incentive mechanism on the responsiveness of the consumer by calibrating our model with publicly available data.
• Anisotropic and crystalline mean curvature flow of mean-convex sets
  
  • Chambolle Antonin
  • Novaga Matteo
  Annali della Scuola Normale Superiore di Pisa, Classe di Scienze, Scuola Normale Superiore, 2022, 23 (2), pp.623-643. We consider a variational scheme for the anisotropic (including crystalline) mean curvature flow of sets with strictly positive anisotropic mean curvature. We show that such condition is preserved by the scheme, and we prove the strict convergence in BV of the time-integrated perimeters of the approximating evolutions, extending a recent result of De Philippis and Laux to the anisotropic setting. We also prove uniqueness of the flat flow obtained in the limit. (10.2422/2036-2145.202005_009)
  DOI : 10.2422/2036-2145.202005_009
• Quantitative particle approximation of nonlinear Fokker-Planck equations with singular kernel
  
  • Olivera Christian
  • Richard Alexandre
  • Tomasevic Milica
  Annali della Scuola Normale Superiore di Pisa, Classe di Scienze, Scuola Normale Superiore, 2022. In this work, we study the convergence of the empirical measure of moderately interacting particle systems with singular interaction kernels. First, we prove quantitative convergence of the time marginals of the empirical measure of particle positions towards the solution of the limiting nonlinear Fokker-Planck equation. Second, we prove the well-posedness for the McKean-Vlasov SDE involving such singular kernels and the convergence of the empirical measure towards it (propagation of chaos). Our results only require very weak regularity on the interaction kernel, which permits to treat models for which the mean field particle system is not known to be well-defined. For instance, this includes attractive kernels such as Riesz and Keller-Segel kernels in arbitrary dimension. For some of these important examples, this is the first time that a quantitative approximation of the PDE is obtained by means of a stochastic particle system. In particular, this convergence still holds (locally in time) for PDEs exhibiting a blow-up in finite time. The proofs are based on a semigroup approach combined with a fine analysis of the regularity of infinite-dimensional stochastic convolution integrals. (10.2422/2036-2145.202105_087)
  DOI : 10.2422/2036-2145.202105_087
• Surrogate-Assisted Bounding-Box approach applied to constrained multi-objective optimisation under uncertainty
  
  • Rivier Mickael
  • Congedo Pietro Marco
  Reliability Engineering and System Safety, Elsevier, 2022, 217, pp.108039. This paper is devoted to tackling constrained multi-objective optimisation under uncertainty problems. A Surrogate-Assisted Bounding-Box approach (SABBa) is formulated here to deal with approximated robustness and reliability measures, which can be adaptively refined. A Bounding-Box is defined as a multi-dimensional product of intervals, centred on the estimated objectives and constraints, that contains the true underlying values. The accuracy of these estimations can be tuned throughout the optimisation so as to reach high levels only on promising designs, which allows quick convergence toward the optimal area. In SABBa, this approach is supplemented with a Surrogate-Assisting (SA) strategy, which permits to further reduce the overall computational cost. The adaptive refinement within the Bounding-Box approach is guided by the computation of the Pareto Optimal Probability (POP) of each box. We first assess the proposed method on several analytical uncertainty-based optimisation test-cases with respect to an a priori metamodel approach in terms of a probabilistic modified Hausdorff distance to the true Pareto optimal set. The method is then applied to three engineering applications: the design of two-bar truss in structural mechanics, the shape optimisation of an Organic Rankine Cycle turbine blade and the design of a thermal protection system for atmospheric reentry. (10.1016/j.ress.2021.108039)
  DOI : 10.1016/j.ress.2021.108039
• Assessment of a non-conservative Residual Distribution scheme for solving a four-equation two-phase system with phase transition
  
  • Bacigaluppi Paola
  • Carlier Julien
  • Pelanti Marica
  • Congedo Pietro Marco
  • Abgrall Rémi
  Journal of Scientific Computing, Springer Verlag, 2022, 90 (1). This work focuses on a four-equation model for simulating two-phase mixtures with phase transition. The main assumption consists in a homogeneous temperature, pressure and velocity fields between the two phases. In particular, we tackle the study of time dependent problems with strong discontinuities and phase transition. This work presents the extension of a non-conservative residual distribution scheme to solve a four-equation two-phase system with phase transition. This non-conservative formulation allows avoiding the classical oscillations obtained by many approaches, that might appear for the pressure profile across contact discontinuities. The proposed method relies on a Finite Volume based Residual Distribution scheme which is designed for an explicit second-order time stepping. We test the non-conservative Residual Distribution scheme on several benchmark problems and assess the results via a cross-validation with the approximated solution obtained via a conservative approach, based on an HLLC solver. Furthermore, we check both methods for mesh convergence and show the effective robustness on very severe test cases, that involve both problems with and without phase transition. (10.1007/s10915-021-01706-6)
  DOI : 10.1007/s10915-021-01706-6
• Design of a mode converter using thin resonant ligaments
  
  • Chesnel Lucas
  • Heleine Jérémy
  • Nazarov Sergei A
  Communications in Mathematical Sciences, International Press, 2022, 20 (2), pp.425–445. The goal of this work is to design an acoustic mode converter. More precisely, the wave number is chosen so that two modes can propagate. We explain how to construct geometries such that the energy of the modes is completely transmitted and additionally the mode 1 is converted into the mode 2 and conversely. To proceed, we work in a symmetric waveguide made of two branches connected by two thin ligaments whose lengths and positions are carefully tuned. The approach is based on asymptotic analysis for thin ligaments around resonance lengths. We also provide numerical results to illustrate the theory. (10.4310/CMS.2022.v20.n2.a6)
  DOI : 10.4310/CMS.2022.v20.n2.a6
• Two dimensional Gross-Pitaevskii equation with space-time white noise
  
  • de Bouard Anne
  • Debussche Arnaud
  • Fukuizumi Reika
  International Mathematics Research Notices, Oxford University Press (OUP), 2022. In this paper we consider the two-dimensional stochastic Gross-Pitaevskii equation, which is a model to describe Bose-Einstein condensation at positive temperature. The equation is a complex Ginzburg-Landau equation with a harmonic potential and an additive space-time white noise. We study the well-posedness of the model using an inhomogeneous Wick renormalization due to the potential, and prove the existence of an invariant measure and of stationary martingale solutions. (10.1093/imrn/rnac137)
  DOI : 10.1093/imrn/rnac137
• SAMBA: a Novel Method for Fast Automatic Model Building in Nonlinear Mixed-Effects Models
  
  • Prague Mélanie
  • Lavielle Marc
  CPT: Pharmacometrics and Systems Pharmacology, American Society for Clinical Pharmacology and Therapeutics ; International Society of Pharmacometrics, 2022, 11 (2). The success of correctly identifying all the components of a nonlinear mixed-effects model is far from straightforward: it is a question of finding the best structural model, determining the type of relationship between covariates and individual parameters, detecting possible correlations between random effects, or also modeling residual errors. We present the SAMBA (Stochastic Approximation for Model Building Algorithm) procedure and show how this algorithm can be used to speed up this process of model building by identifying at each step how best to improve some of the model components. The principle of this algorithm basically consists in 'learning something' about the 'best model', even when a 'poor model' is used to fit the data. A comparison study of the SAMBA procedure with SCM and COSSAC show similar performances on several real data examples but with a much-reduced computing time. This algorithm is now implemented in Monolix and in the R package Rsmlx. (10.1002/psp4.12742)
  DOI : 10.1002/psp4.12742
• Convergence to line and surface energies in nematic liquid crystal colloids with external magnetic field
  
  • Alouges François
  • Chambolle Antonin
  • Stantejsky Dominik
  Calculus of Variations and Partial Differential Equations, Springer Verlag, 2022, 63 (5), pp.129. We use the Landau-de Gennes energy to describe a particle immersed into nematic liquid crystals with a constant applied magnetic field. We derive a limit energy in a regime where both line and point defects are present, showing quantitatively that the close-to-minimal energy is asymptotically concentrated on lines and surfaces nearby or on the particle. We also discuss regularity of minimizers and optimality conditions for the limit energy. (10.1007/s00526-024-02717-5)
  DOI : 10.1007/s00526-024-02717-5
• The mesoscopic geometry of sparse random maps
  
  • Curien Nicolas
  • Kortchemski Igor
  • Marzouk Cyril
  Journal de l'École polytechnique — Mathématiques, École polytechnique, 2022, 9, pp.1305-1345. (10.5802/jep.207)
  DOI : 10.5802/jep.207
• ON THE DISCRETIZATION OF DISCONTINUOUS SOURCES OF HYPERBOLIC BALANCE LAWS
  
  • Pichard Teddy
  , 2022. We focus on a toy problem which corresponds to a simplification of a boiling twophase flow model. This model is a hyperbolic system of balance laws with a source term defined as a discontinuous function of the unknown. Several discretizations of this source terms are studied, and we illustrate their capacity to capture steady states. (10.23967/eccomas.2022.172)
  DOI : 10.23967/eccomas.2022.172
• Local-Global MCMC kernels: the best of both worlds
  
  • Samsonov Sergey
  • Lagutin Evgeny
  • Gabrié Marylou
  • Durmus Alain
  • Naumov Alexey
  • Moulines Eric
  , 2022. Recent works leveraging learning to enhance sampling have shown promising results, in particular by designing effective non-local moves and global proposals. However, learning accuracy is inevitably limited in regions where little data is available such as in the tails of distributions as well as in high-dimensional problems. In the present paper we study an Explore-Exploit Markov chain Monte Carlo strategy ($Ex^2MCMC$) that combines local and global samplers showing that it enjoys the advantages of both approaches. We prove $V$-uniform geometric ergodicity of $Ex^2MCMC$ without requiring a uniform adaptation of the global sampler to the target distribution. We also compute explicit bounds on the mixing rate of the Explore-Exploit strategy under realistic conditions. Moreover, we also analyze an adaptive version of the strategy ($FlEx^2MCMC$) where a normalizing flow is trained while sampling to serve as a proposal for global moves. We illustrate the efficiency of $Ex^2MCMC$ and its adaptive version on classical sampling benchmarks as well as in sampling high-dimensional distributions defined by Generative Adversarial Networks seen as Energy Based Models. We provide the code to reproduce the experiments at the link: https://github.com/svsamsonov/ex2mcmc_new.
• Long-time behaviour of entropic interpolations
  
  • Clerc Gauthier
  • Conforti Giovanni
  • Gentil Ivan
  Potential Analysis, Springer Verlag, 2022. In this article we investigate entropic interpolations. These measure valued curves describe the optimal solutions of the Schrödinger problem [Sch31], which is the problem of finding the most likely evolution of a system of independent Brownian particles conditionally to observations. It is well known that in the short time limit entropic interpolations converge to the McCann-geodesics of optimal transport. Here we focus on the long-time behaviour, proving in particular asymptotic results for the entropic cost and establishing the convergence of entropic interpolations towards the heat equation, which is the gradient flow of the entropy according to the Otto calculus interpretation. Explicit rates are also given assuming the Bakry-Émery curvature-dimension condition. In this respect, one of the main novelties of our work is that we are able to control the long time behavior of entropic interpolations assuming the CD(0, n) condition only. (10.1007/s11118-021-09961-w)
  DOI : 10.1007/s11118-021-09961-w
• Extended mean field control problem: a propagation of chaos result
  
  • Djete Mao Fabrice
  Electronic Journal of Probability, Institute of Mathematical Statistics (IMS), 2022, 27 (none). (10.1214/21-EJP726)
  DOI : 10.1214/21-EJP726
• Points and lines configurations for perpendicular bisectors of convex cyclic polygons
  
  • Melotti Paul
  • Ramassamy Sanjay
  • Thévenin Paul
  The Electronic Journal of Combinatorics, Open Journal Systems, 2022, 29 (1), pp.P1.59. We characterize the topological configurations of points and lines that may arise when placing n points on a circle and drawing the n perpendicular bisectors of the sides of the corresponding convex cyclic n-gon. We also provide a functional central limit theorem describing the shape of a large realizable configuration of points and lines taken uniformly at random among realizable configurations.
• Multiresolution-based mesh adaptation and error control for lattice Boltzmann methods with applications to hyperbolic conservation laws
  
  • Bellotti Thomas
  • Gouarin Loïc
  • Graille Benjamin
  • Massot Marc
  SIAM Journal on Scientific Computing, Society for Industrial and Applied Mathematics, 2022, 44 (4), pp.A2599-A2627. Lattice Boltzmann Methods (LBM) stand out for their simplicity and computational efficiency while offering the possibility of simulating complex phenomena. While they are optimal for Cartesian meshes, adapted meshes have traditionally been a stumbling block since it is difficult to predict the right physics through various levels of meshes. In this work, we design a class of fully adaptive LBM methods with dynamic mesh adaptation and error control relying on multiresolution analysis. This wavelet-based approach allows to adapt the mesh based on the regularity of the solution and leads to a very efficient compression of the solution without loosing its quality and with the preservation of the properties of the original LBM method on the finest grid. This yields a general approach for a large spectrum of schemes and allows precise error bounds, without the need for deep modifications on the reference scheme. An error analysis is proposed. For the purpose of assessing the approach, we conduct a series of test-cases for various schemes and scalar and systems of conservation laws, where solutions with shocks are to be found and local mesh adaptation is especially relevant. Theoretical estimates are retrieved while a reduced memory footprint is observed. It paves the way to an implementation in a multi-dimensional framework and high computational efficiency of the method for both parabolic and hyperbolic equations, which is the subject of a companion paper. (10.1137/21M140256X)
  DOI : 10.1137/21M140256X
• Docent: A content-based recommendation system to discover contemporary art
  
  • Fosset Antoine
  • El-Mennaoui Mohamed
  • Rebei Amine
  • Calligaro Paul
  • Di Maria Elise Farge
  • Nguyen-Ban Hélène
  • Rea Francesca
  • Vallade Marie-Charlotte
  • Vitullo Elisabetta
  • Zhang Christophe
  • Charpiat Guillaume
  • Rosenbaum Mathieu
  , 2022. Recommendation systems have been widely used in various domains such as music, films, e-shopping etc. After mostly avoiding digitization, the art world has recently reached a technological turning point due to the pandemic, making online sales grow significantly as well as providing quantitative online data about artists and artworks. In this work, we present a content-based recommendation system on contemporary art relying on images of artworks and contextual metadata of artists. We gathered and annotated artworks with advanced and art-specific information to create a completely unique database that was used to train our models. With this information, we built a proximity graph between artworks. Similarly, we used NLP techniques to characterize the practices of the artists and we extracted information from exhibitions and other event history to create a proximity graph between artists. The power of graph analysis enables us to provide an artwork recommendation system based on a combination of visual and contextual information from artworks and artists. After an assessment by a team of art specialists, we get an average final rating of 75% of meaningful artworks when compared to their professional evaluations.
• Algorithmic market making in dealer markets with hedging and market impact
  
  • Barzykin Alexander
  • Bergault Philippe
  • Guéant Olivier
  Mathematical Finance, Wiley, 2022. In dealer markets, dealers provide prices at which they agree to buy and sell the assets and securities they have in their scope. With ever increasing trading volume, this quoting task has to be done algorithmically in most markets such as foreign exchange markets or corporate bond markets. Over the last ten years, many mathematical models have been designed that can be the basis of quoting algorithms in dealer markets. Nevertheless, in most (if not all) models, the dealer is a pure internalizer, setting quotes and waiting for clients. However, on many dealer markets, dealers also have access to an inter-dealer market or even public trading venues where they can hedge part of their inventory. In this paper, we propose a model taking this possibility into account, therefore allowing dealers to externalize part of their risk. The model displays an important feature well known to practitioners that within a certain inventory range the dealer internalizes the flow by appropriately adjusting the quotes and starts externalizing outside of that range. The larger the franchise, the wider is the inventory range suitable for pure internalization. The model is illustrated numerically with realistic parameters for USDCNH spot market.
• Topology optimization of supports with imperfect bonding in additive manufacturing
  
  • Allaire Grégoire
  • Bogosel Beniamin
  • Godoy Matías
  Structural and Multidisciplinary Optimization, Springer Verlag, 2022. Supports are an important ingredient of the building process of structures by additive manufacturing technologies. They are used to reinforce overhanging regions of the desired structure and/or to facilitate the mitigation of residual thermal stresses due to the extreme heat flux produced by the source term (laser beam). Very often, supports are, on purpose, weakly connected to the built structure for easing their removal. In this work, we consider an imperfect interface model for which the interaction between supports and the built structure is not ideal, meaning that the displacement is discontinuous at the interface while the normal stress is continuous and proportional to the jump of the displacement. The optimization process is based on the level set method, body-fitted meshes and the notion of shape derivative using the adjoint method. We provide 2-d and 3-d numerical examples, as well as a comparison with the usual perfect interface model. Completely different designs of supports are obtained with perfect or imperfect interfaces.
• Spatio-temporal mixture process estimation to detect population dynamical changes
  
  • Pruilh Solange
  • Jannot Anne-Sophie
  • Allassonnière Stéphanie
  Artificial Intelligence in Medicine, Elsevier, 2022, 126, pp.102258. Population monitoring is a challenge in many areas such as public health or ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data evolution. Assuming that mixture models can correctly model populations, we propose new versions of the Expectation-Maximization algorithm to better estimate both the number of clusters together with their parameters. We then combine these algorithms with a temporal statistical model, allowing to detect dynamical changes in population distributions, and name it a spatio-temporal mixture process (STMP). We test STMP on synthetic data, and consider several different behaviors of the distributions, to adjust this process. Finally, we validate STMP on a real data set of positive diagnosed patients to corona virus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes. (10.1016/j.artmed.2022.102258)
  DOI : 10.1016/j.artmed.2022.102258
• Gâteaux type path-dependent PDEs and BSDEs with Gaussian forward processes
  
  • Barrasso Adrien
  • Russo Francesco
  Stochastics and Dynamics, World Scientific Publishing, 2022, 22, pp.2250007,. We are interested in path-dependent semilinear PDEs, where the derivatives are of Gâteaux type in specific directions k and b, being the kernel functions of a Volterra Gaussian process X. Under some conditions on k, b and the coefficients of the PDE, we prove existence and uniqueness of a decoupled mild solution, a notion introduced in a previous paper by the authors. We also show that the solution of the PDE can be represented through BSDEs where the forward (underlying) process is X. (10.1142/S0219493722500071)
  DOI : 10.1142/S0219493722500071