3 papers
cs.LG2026
DeSyR: A Decoupled Symbolic Recovery Framework with PINN-Guided Structure Search and Physics-Informed Coefficient Refinement
Pancheng Niu, Jun Guo, Qiaolin He +2
Recovering compact explicit solutions from neural approximations is challenging when imperfect teacher data guide symbolic topology search and coefficient estimation. We present De…
cs.LG2026
Architecture-Optimization Co-Design for Physics-Informed Neural Networks Via Attentive Representations and Conflict-Resolved Gradients
Pancheng Niu, Jun Guo, Qiaolin He +2
Physics-Informed Neural Networks (PINNs) provide a learning-based framework for solving partial differential equations (PDEs) by embedding governing physical laws into neural netwo…
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…