arxiv
PublishedJune 5, 2026 at 4:00 AM
—neutral
Curvature-aware dynamic precision approach for physics-informed neural networks
Publisher summary· verbatim
arXiv:2606.04736v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have become a promising framework for simulating partial differential equations (PDEs) by embedding physical laws directly into neural network training. However, recent studies show that PINN optimisation is s
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivSFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning15harxivOptical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning15harxivDynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models15harxivTemporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents15hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗