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LFNO: Bridging Laplace and Fourier via Transient-Steady Decomposition

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arXiv:2606. 07601v1 Announce Type: new Abstract: We introduce the Laplace-Fourier Neural Operator (LFNO), a unified framework for modeling dynamical systems across transient and steady-state regimes by integrating the spectral advantages of Laplace and Fourier Neural Operators.

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Authors: Jeongun Ha, Sanga Yoon, Donghun Lee
Released: 2026-06-09 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because LFNO: Bridging Laplace and Fourier via Transient-Steady Decomposition overlaps with clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: eval, rate, position, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.07601

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