1 code implementation • 6 Feb 2024 • Raphaël Carpintero Perez, Sébastien da Veiga, Josselin Garnier, Brian Staber
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solving partial differential equations, or predicting material properties.
1 code implementation • 22 May 2023 • Fabien Casenave, Brian Staber, Xavier Roynard
When learning simulations for modeling physical phenomena in industrial designs, geometrical variabilities are of prime interest.
1 code implementation • NeurIPS 2023 • Clément Bénard, Brian Staber, Sébastien da Veiga
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC).
no code implementations • 8 Jun 2022 • Brian Staber, Sébastien da Veiga
Due to the growing adoption of deep neural networks in many fields of science and engineering, modeling and estimating their uncertainties has become of primary importance.