4 papers · 1 filter
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…
RD-JEPA: Predictive latent pretraining for few-trajectory transfer across reaction--diffusion equations
Chenhao Si, Ming Yan
Learning surrogates for time-dependent partial differential equations often requires a new simulation corpus when the governing operator changes. We introduce RD-JEPA, a joint-embe…
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…
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…