Work

Response Theory via Generative Score Modeling

Year: 2024

Type: article

Abstract: We introduce an approach for analyzing the responses of dynamical systems to external perturbations that combines score-based generative modeling with the generalized fluctuation-dissipation theorem. The methodology enables accurate estimation of system responses, including those with non-Gaussian statistics. We numerically validate our approach using time-series data from three different stochastic partial differential equations of increasing complexity: an Ornstein-Uhlenbeck process with spatially correlated noise, a modified stochastic Allen-Cahn equation, and the 2D Navier-Stokes equations. We demonstrate the improved accuracy of the methodology over conventional methods and discuss its potential as a versatile tool for predicting the statistical behavior of complex dynamical systems.

Source: Physical Review Letters

DOI: 10.1103/physrevlett.133.267302

Authors: Ludovico Theo Giorgini, Katherine Deck, Tobias Bischoff, Andre Souza

Institutions: Nordic Institute for Theoretical Physics, Massachusetts Institute of Technology, California Institute of Technology

Language: English

Topic: Advanced Thermodynamics and Statistical Mechanics

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,750

Funders: Cisco Systems, Vetenskapsrådet