4 papers
ATLAS-NN: Adaptive Transfer Learnable Symplectic-aware Neural Network for Long-Time Hamiltonian Dynamics
Changhong Mou, Dinghua Xu, Xiyue Zuo +2
Modeling Hamiltonian systems over long temporal intervals remains a significant challenge due to intrinsic multiscale structures and rapid nonlinear transitions. While Hamiltonian…
Physics-Aligned Canonical Equivariant Fourier Neural Operator under Symmetry-Induced Shifts
Jiaxiao Xu, Changhong Mou, Yeyu Zhang +1
Neural operators approximate PDE solution maps, but they need not respect the symmetries of the governing equation. In out-of-distribution (OOD) regimes, a standard neural operator…
PIP Net: Physics-informed Partition Penalty Deep Operator Network
Hongjin Mi, Huiqiang Lun, Changhong Mou +1
Operator learning has become a powerful tool for accelerating the solution of parameterized partial differential equations (PDEs), enabling rapid prediction of full spatiotemporal…
PAS-Net: Physics-informed Adaptive Scale Deep Operator Network
Changhong Mou, Yeyu Zhang, Xuewen Zhu +1
Nonlinear physical phenomena often show complex multiscale interactions; motivated by the principles of multiscale modeling in scientific computing, we propose PAS-Net, a physics-i…