313 citations · 313 across the 3 of their papers we have counts for
3 papers
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…
cond-mat.stat-mech2025
Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics
Weilun Qiu, Shenglin Huang, Celia Reina
Thermodynamics with internal variables is a common approach in continuum mechanics to model inelastic (i.e., non-equilibrium) material behavior. While this approach is computationa…
math.NA2020★ 313 cited
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
Weiqi Ji, Weilun Qiu, Zhiyu Shi +2
Recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the n…