1 citations · 1 across the 1 of their papers we have counts for
2 papers
stat.ML2026
Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification
Christian Soize
We introduce stochastic physics-informed neural networks (SPINNs) for stochastic boundary-value problems (SBVPs) with random operators. The method enforces physics through a struct…
stat.ML2024★ 1 cited
Transient anisotropic kernel for probabilistic learning on manifolds
Christian Soize, Roger Ghanem
PLoM (Probabilistic Learning on Manifolds) is a method introduced in 2016 for handling small training datasets by projecting an Itô equation from a stochastic dissipative Hamiltoni…