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

Excerpt

arXiv:2610.06798v2 Announce Type: replace-cross 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-dissipation relation, and decomposes over the stocha