Signals 4 · free daily AI digest

NEXT: Physics-Informed Neuro-Spectral Exponential Time Differencing Architectures

Paper recorded by Signals 4 on 2026-09-25 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-25 on arXiv · recorded by Signals 4 on 2026-09-28

Category: cs.LG · 机器学习 · first seen 2026-09-28

Abstract

Physics-Informed Neural Networks (PINNs) build neural representations of time-dependent PDE solutions, naturally incorporating physics knowledge and observational data, which makes them well suited to both forward and inverse PDE problems. PINNs, however, are known to suffer from spectral bias and lack of causality. Neuro-Spectral Architectures (NeuSA), a recently proposed alternative to PINNs, mi

Read on arXiv →

#13 most recent of 310 cs.LG papers we have recorded · ↑ newer: BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representat · ↓ older: HySTAR: Anchored Hypergraphs for Stable Credit Assignment in Cooperati
Cite this page: NEXT: Physics-Informed Neuro-Spectral Exponential Time Differencing Architectures: the #13 most recent of 310 cs.LG papers we have recorded (as of 2026-09-25). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/next-physics-informed-neuro-spectral-exponential-time-differencing-architectures.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.LG papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions