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nonlinear partial differential equations

topic2 events

papersTODAY 04:00 UTC

Physics-Constrained Neural Surrogate Models Domain Growth Under Conserved Kinetics

A revised arXiv preprint presents a neural surrogate model that incorporates physical constraints to predict how domains grow in systems governed by conserved kinetics. Such systems are typically described by nonlinear partial differential equations, which are costly to solve, and learned surrogates aim to offer faster alternatives. The work targets physics, chemistry, and biology applications where accurate spatiotemporal prediction matters.

research/neural-surrogates/physics-informed/simulation

papersSEP 10 04:00 UTC

Gradient-guided Gaussian adaptive sampling proposed for training physics-informed neural networks

A new arXiv paper introduces 3GAS-PINNs, a variant of physics-informed neural networks that uses gradient-guided Gaussian adaptive sampling to place collocation points. The method targets common weaknesses in PINNs on nonlinear partial differential equations, such as slow convergence, gradient imbalance, and poor resolution of demanding regions. By concentrating sampling where it matters most, the approach aims to improve training efficiency and solution accuracy.

pinns/pdes/adaptive-sampling/training