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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…