3 papers
physics.comp-ph2025
Boundary-Informed Method of Lines for Physics Informed Neural Networks
Maximilian Cederholm, Siyao Wang, Haochun Wang +2
We propose a hybrid solver that fuses the dimensionality-reduction strengths of the Method of Lines (MOL) with the flexibility of Physics-Informed Neural Networks (PINNs). Instead…
physics.comp-ph2025
Velocity-Inferred Hamiltonian Neural Networks: Learning Energy-Conserving Dynamics from Position-Only Data
Ruichen Xu, Zongyu Wu, Luoyao Chen +5
Data-driven modeling of physical systems often relies on learning both positions and momenta to accurately capture Hamiltonian dynamics. However, in many practical scenarios, only…
math.OC2025
The Impact of Move Schemes on Simulated Annealing Performance
Ruichen Xu, Haochun Wang, Yuefan Deng
Designing an effective move-generation function for Simulated Annealing (SA) in complex models remains a significant challenge. In this work, we present a combination of theoretica…