3 papers
cs.LG2026
Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification
Handi Zhang, Adrienne M. Propp, Brooks Kinch +2
Recent advances in scientific machine learning provide a means of near-real-time solution to partial differential equations (PDEs), but lack the theoretical underpinnings of conven…
cs.LG2026
A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting
Brooks Kinch, Xiaozhe Hu, Yilong Huang +6
For autoregressive modeling of chaotic dynamical systems over long time horizons, the stability of both training and inference is a major challenge in building scientific foundatio…
cs.LG2026
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs
Adrienne M. Propp, Jonas A. Actor, Elise Walker +3
Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfa…