3 citations · 3 across the 5 of their papers we have counts for
5 papers
Structure-preserving uncertainty quantification for GENERIC dynamics
Zequn He, Celia Reina
Structure-preserving machine learning embeds physical structure directly into model architectures, yet uncertainty quantification (UQ) for such hard-constrained models remains limi…
Nonlinear GENERIC-Embedded Neural Networks (N-GENNs): Learning GENERIC dynamics with non-quadratic dissipation potentials
Vojtěch Votruba, Zequn He, Weilun Qiu +2
We introduce Nonlinear GENERIC-Embedded Neural Networks (N-GENNs), a deep learning framework for discovering evolution equations of systems governed by the nonlinear GENERIC formal…
SPIEDiff: robust learning of long-time macroscopic dynamics from short-time particle simulations with quantified epistemic uncertainty
Zequn He, Celia Reina
The data-driven discovery of long-time macroscopic dynamics and thermodynamics of dissipative systems with particle fidelity is hampered by significant obstacles. These include the…
EVODMs: variational learning of PDEs for stochastic systems via diffusion models with quantified epistemic uncertainty
Zequn He, Celia Reina
We present Epistemic Variational Onsager Diffusion Models (EVODMs), a machine learning framework that integrates Onsager's variational principle with diffusion models to enable the…
Statistical-Physics-Informed Neural Networks (Stat-PINNs): A Machine Learning Strategy for Coarse-graining Dissipative Dynamics
Shenglin Huang, Zequn He, Nicolas Dirr +2
Machine learning, with its remarkable ability for retrieving information and identifying patterns from data, has emerged as a powerful tool for discovering governing equations. It…