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A Response Theory Probe for Learned Stochastic AI Simulators, Tested on Lorenz-63

Paper recorded by Signals 4 on 2026-10-05 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-05 on arXiv · recorded by Signals 4 on 2026-10-06

Category: cs.LG · 机器学习 · first seen 2026-10-06

Abstract

Machine-learning emulators of chaotic and stochastic systems are usually validated on forecast skill and long-run statistics. Neither certifies that an emulator responds correctly to forcing, the property that projection and attribution studies rely on. Linear response theory makes this testable: the forced response follows from unperturbed correlations through a generalized fluctuation-dissipatio

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#378 most recent of 762 cs.LG papers we have recorded · ↑ newer: H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Pl · ↓ older: Round-Trip KNN Clustering: multiscale hierarchical cluster detection o
Cite this page: A Response Theory Probe for Learned Stochastic AI Simulators, Tested on Lorenz-63: the #378 most recent of 762 cs.LG papers we have recorded (as of 2026-10-05). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-response-theory-probe-for-learned-stochastic-ai-simulators-tested-on-lorenz-63.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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