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cs.LG2026
Green-Routed Neural Operators: Physics Determines Where the Network Reads
Chenhao Si, Ming Yan
We identify a mismatch between the physical role of transport fields in many PDEs and their usual role in neural operators: PDEs use them to select read coordinates, whereas neural…
cs.LG2026
From Non-Convex Self-Concordant Regularization to Scalable Quasi-Newton Training of PINNs
Chenhao Si, Kang An, Shiqian Ma +1
Physics-informed neural networks (PINNs) often require high-accuracy quasi-Newton refinement to obtain reliable partial differential equation solutions, but their residual objectiv…
cs.LG2026
Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
Kang An, Chenhao Si, Shiqian Ma +1
Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on challenging partial differential equations due to the ani…