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…
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…
Newton Method for Fixed-Support Doubly Entropic Wasserstein Barycenter
Jianting Pan, Sirong Dai, Lei Yang +3
We study the fixed-support doubly regularized Wasserstein barycenter problem. Using the semi-dual formulation of entropic optimal transport, we reformulate the problem as a smooth,…
Inexact Bregman Sparse Newton Method for Efficient Optimal Transport
Jianting Pan, Ji'an Li, Ming Yan
Computing exact Optimal Transport (OT) distances for large-scale datasets is computationally prohibitive. While entropy-regularized alternatives offer speed, they sacrifice precisi…