Author

Celia Reina

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Preprint Aug 2026

Structure-preserving uncertainty quantification for GENERIC dynamics

S-PENNs preserve the structural constraints of a pretrained model by attaching lightweight epinets to its constrained components, ensuring that every sampled realization remains physically admissible by construction, and yields thermodynamically consistent rollouts that preserve the first and second laws.

Zequn He, Celia Reina · 0 citations

#machine learning Open access May 2026

Nonlinear GENERIC-Embedded Neural Networks (N-GENNs): Learning GENERIC dynamics with non-quadratic dissipation potentials

Nonlinear GENERIC-Embedded Neural Networks (N-GENNs) is introduced, a deep learning framework for discovering evolution equations of systems governed by the nonlinear GENERIC formalism, and its ability to accurately infer thermodynamically consistent models from data for systems incorporating both conservative and nonl...

Vojtěch Votruba, Zequn He, Weilun Qiu et al. · Computer Methods in Applied... · 1 citation