aiqbench analysis
The paper extends a method for building stochastic reduced-order models of physical systems so that the mobility matrix can vary with state, and it fits that mobility to reproduce finite-lag correlations. Coefficients come from a least-squares fit in the simple case, and a neural network handles the general case. Its validation is on a Cox-Ingersoll-Ross process, a 2D nonequilibrium diffusion, and a 36-dimensional spin chain, with the claimed advantage being lower sensitivity to coarse sampling than Kramers-Moyal estimates. Readers should note that the results are benchmarked on modest test systems, and that the main contribution is a statistical-physics estimation technique that uses machine learning tools.
Score-Based Modeling of Effective Langevin Dynamics with State-Dependent Mobility
arXiv:2604.23952v3 Announce Type: replace-cross Abstract: In a previous work [L. T. Giorgini, Phys. Rev. E 114, L012102 (2026)], we introduced stochastic reduced-order models that reproduce by construction the stationary density and short-time correlations of the observed variables, using a constant mobility matrix and the score, the gradient of the logarithm of this density. Here we generalize the framework to state-dependent mobilities, calibrated so that the model also reproduces the finite-lag correlation functions of chosen target observables. The calibration uses exact finite-lag forms of the generalized fluctuation-dissipation theorem, which express time derivatives of lagged correlations as averages over observed pairs of states and are linear in the mobility. When the mobility is expanded on a basis of known fields, its coefficients follow from a least-squares problem involving only the stationary score, and the observables and basis are selected by data-driven diagnostics. Otherwise, a neural-network mobility is calibrated through the conditional score of the finite-time transition density. We validate the approach analytically on a Cox-Ingersoll-Ross process and numerically on a two-dimensional nonequilibrium diffusion with multiplicative noise and a 36-dimensional soft-spin Landau-Lifshitz chain. Both methods reproduce the stationary statistics and the target correlations accurately. Because the identities can be integrated between consecutive lags, so that no time derivatives need to be estimated, the recovered drift and diffusion are much less sensitive to coarse sampling than Kramers-Moyal estimates, which require short sampling intervals. Calibration requires no simulation of the model and applies to high-dimensional systems.
Abstract:In a previous work [L. T. Giorgini, Phys. Rev. E 114, L012102 (2026)], we introduced stochastic reduced-order models that reproduce by construction the stationary density and short-time correlations of the observed variables, using a constant mobility matrix and the score, the gradient of the logarithm of this density. Here we generalize the framework to state-dependent mobilities, calibrated so that the model also reproduces the finite-lag correlation functions of chosen target observables. The calibration uses exact finite-lag forms of the generalized fluctuation-dissipation theorem, which express time derivatives of lagged correlations as averages over observed pairs of states and are linear in the mobility. When the mobility is expanded on a basis of known fields, its coefficients follow from a least-squares problem involving only the stationary score, and the observables and basis are selected by data-driven diagnostics. Otherwise, a neural-network mobility is calibrated through the conditional score of the finite-time transition density. We validate the approach analytically on a Cox-Ingersoll-Ross process and numerically on a two-dimensional nonequilibrium diffusion with multiplicative noise and a 36-dimensional soft-spin Landau-Lifshitz chain. Both methods reproduce the stationary statistics and the target correlations accurately. Because the identities can be integrated between consecutive lags, so that no time derivatives need to be estimated, the recovered drift and diffusion are much less sensitive to coarse sampling than Kramers-Moyal estimates, which require short sampling intervals. Calibration requires no simulation of the model and applies to high-dimensional systems.
46 pages, 10 figures; revised version. Code: this https URL (v2-revised-submission)
Machine Learning (stat.ML); Machine Learning (cs.LG); Chaotic Dynamics (nlin.CD)
arXiv:2604.23952 [stat.ML]
(or arXiv:2604.23952v3 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2604.23952 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 345 words