📰 Paper Digest
New arXiv papers in Physics-AI / Scientific ML, filtered weekly, de-duplicated, each with an editorially written reason to read. Metadata only — abstracts are never reproduced.
RSS Subscribe via RSS to get each new issue automatically. Week 43 10 papers
Week 43 · Norm-optimal model order reduction in reproducing kernel Hilbert spaces
Week 43 Physics AI Digest — 10 papers on physics-informed learning, neural operators and surrogate modelling.
- This week: 10 papers, focusing on pde, physics-informed, partial differential equation
- Recommendation reasons are auto-generated from paper abstracts; see each entry for details
Coverage: 2026-09-27 → 2026-10-20 Updated 2026-10-22
Week 42 8 papers
Week 42 · When PINNs Fail, and Where Neural Operators Break
These eight papers share one temperament: instead of reporting another accuracy bump, they dissect when physics-constrained methods actually break — conflicting gradients, axis singularities, non-commuting nonlinear terms under coarse-graining, and whether "reuse" really transfers.
- PINN failure modes get a systematic account: conflicting residual / boundary / interface gradients after domain decomposition, mitigated by conflict-free gradient projection
- Two papers question the premise of physics constraints themselves: under coarse-graining or stochastic forcing, the fitted fields do not satisfy the original equations
- Neural operators push into hard geometries: joint spatiotemporal spectral operators on irregular domains, no domain warping or interpolation
Coverage: 2026-08-30 → 2026-10-07 Updated 2026-10-12