Paper

Fourier Feature Pyramids for Physics-Informed Neural Networks

Unreadunread

AI summary

arXiv:2605. 24278v1 Announce Type: new Abstract: We present an improved neural field architecture for solving partial differential equations (PDEs).

Reading status

Priority

About this paper

Authors: Brandon Zhao, Yixuan Wang, Jonathan T. Barron, Katherine L. Bouman, Dor Verbin, Pratul P. Srinivasan
Released: 2026-05-26 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Fourier Feature Pyramids for Physics-Informed Neural Networks overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, rect, eval, rate, control, full, chain, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.24278

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Fourier Feature Pyramids for Physics-Informed Neural Networks. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.