M. Klasky

Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws

A variational latent neural field framework that integrates Gaussian process-inspired surrogates is developed, enabling estimation of predictive confidence for both in-distribution and out-of-distribution parameter regimes and shows that conservation-structure preserving latent representations improve robustness to degraded training data while maintaining competitive predictive accuracy and uncertainty quantification capability.