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cs.LG2026
DeSyR: A decoupled symbolic recovery framework for PDEs with PINN-guided structure discovery and physics-based coefficient refinement
Pancheng Niu, Jun Guo, Qiaolin He +2
Recovering explicit symbolic solutions of partial differential equations (PDEs) from physics-informed neural networks (PINNs) is attractive, yet selecting and fitting expressions a…
cs.LG2026
Architecture--Optimization Co-Design for Physics-Informed Neural Networks via Layer-wise Coordinate Adaptation and Gradient Conflict Resolution
Pancheng Niu, Jun Guo, Qiaolin He +2
Physics-informed neural networks (PINNs) can be limited by coordinate representations and conflicting gradients from heterogeneous physical constraints. We propose Architecture--Co…
cs.LG2024
Improved physics-informed neural network in mitigating gradient related failures
Pancheng Niu, Yongming Chen, Jun Guo +3
Physics-informed neural networks (PINNs) integrate fundamental physical principles with advanced data-driven techniques, driving significant advancements in scientific computing. H…