ESAIM: Proceedings and Surveys

Open Access

Editorial

CEMRACS 2023 - Scientific machine learning p. 1

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581001

Open Access

A dynamical neural Galerkin scheme for filtering problems p. 2

Joubine Aghili, Joy Zialesi Atokple, Marie Billaud-Friess, Guillaume Garnier, Olga Mula and Norbert Tognon

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581002

Open Access

Learning local Dirichlet-to-Neumann maps of nonlinear elliptic PDEs with rough coefficients p. 16

Miranda Boutilier, Konstantin Brenner and Larissa Miguez

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581016

Open Access

Benchmarking PDE problems via machine learning for enhanced computational modeling p. 33

Bahae-Eddine Madir and Antoine Lechevallier

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581033

Open Access

Comparison between tensor methods and neural networks in electronic structure calculations p. 55

Mathias Dus, Geneviève Dusson, Virginie Ehrlacher, Clément Guillot and Joel Pascal Soffo Wambo

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581055

Open Access

Analysis and optimization of a liquid-vapor thermohydraulic model p. 77

Philippe Helluy and Gauthier Lazare

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581077

Open Access

Surrogate modeling of interactions in microbial communities through Physics-Informed Neural Networks p. 104

Paguiel Javan Hossie, Béatrice Laroche, Thibault Malou, Lucas Perrin, Thomas Saigre and Lorenzo Sala

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581104

Open Access

Volume-preserving transformers for learning time series data with structure p. 123

Benedikt Brantner, Michael Kraus, Zeyuan Li and Guillaume de Romemont

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581123

Open Access

Predicting ultimate hydrogen production and residual volume during cyclic underground hydrogen storage in porous media using machine learning p. 145

Raymond Mushabe, Jean Donald Minougou, David Landa-Marbán, Birane Kane and Tor Harald Sandve

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581145

Open Access

Generalizing the SINDy approach with nested neural networks p. 168

Camilla Fiorini, Clément Flint, Louis Fostier, Emmanuel Franck, Reyhaneh Hashemi, Victor Michel-Dansac and Wassim Tenachi

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581168

Open Access

Multidimensional integration using machine learning and Monte Carlo methods for acoustic predictions p. 193

Théo Coiffard, Victoria Eshomomoh Laoye, Yolhan Mannes, Shyam Mohan, Subbiah Pillai and Nicolas Trafny

Published online: 10 October 2025

DOI: https://doi.org/10.1051/proc/202581193

ESAIM: Proceedings and Surveys

Volume 81 (2025)

CEMRACS 2023 - Scientific Machine Learning

CEMRACS 2023 - Scientific machine learning p. 1

A dynamical neural Galerkin scheme for filtering problems p. 2

Learning local Dirichlet-to-Neumann maps of nonlinear elliptic PDEs with rough coefficients p. 16

Benchmarking PDE problems via machine learning for enhanced computational modeling p. 33

Comparison between tensor methods and neural networks in electronic structure calculations p. 55

Analysis and optimization of a liquid-vapor thermohydraulic model p. 77

Surrogate modeling of interactions in microbial communities through Physics-Informed Neural Networks p. 104

Volume-preserving transformers for learning time series data with structure p. 123

Predicting ultimate hydrogen production and residual volume during cyclic underground hydrogen storage in porous media using machine learning p. 145

Generalizing the SINDy approach with nested neural networks p. 168

Multidimensional integration using machine learning and Monte Carlo methods for acoustic predictions p. 193