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20232026
most citedStatistical-Physics-Informed Neural Networks (Stat-PINNs): A Machine Learning Strategy for Coarse-graining Dissipative Dynamics

3 citations · 3 across the 5 of their papers we have counts for

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cs.LG2026

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…

physics.comp-ph2026

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…