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Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
Luning Sun, Daniel Zhengyu Huang, Hao Sun +1
Nonlinear dynamics are ubiquitous in science and engineering applications, but the physics of most complex systems is far from being fully understood. Discovering interpretable gov…
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search
Fangzheng Sun, Yang Liu, Jian-Xun Wang +1
Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from l…
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
Xin-Yang Liu, Min Zhu, Lu Lu +2
Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed…
Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics
Pan Du, Xiaozhi Zhu, Jian-Xun Wang
Optimization and uncertainty quantification have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling a…
Physics-Informed Deep Learning for Solving Phonon Boltzmann Transport Equation with Large Temperature Non-Equilibrium
Ruiyang Li, Jian-Xun Wang, Eungkyu Lee +1
Phonon Boltzmann transport equation (BTE) is a key tool for modeling multiscale phonon transport, which is critical to the thermal management of miniaturized integrated circuits, b…
Predicting Physics in Mesh-reduced Space with Temporal Attention
Xu Han, Han Gao, Tobias Pfaff +2
Graph-based next-step prediction models have recently been very successful in modeling complex high-dimensional physical systems on irregular meshes. However, due to their short te…