SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics

2023/02/07 by Panos Stinis, Constantinos Daskalakis, Stinis, Panos +3 · 2 citations
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #Data Analysis #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.2302.03663

openalex publication_date 2023/02/07 · openalex created_date 2023/02/10 · openalex updated_date 2026/08/01

Abstract

We introduce adversarial learning methods for data-driven generative modeling of the dynamics of n^th-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. We introduce different formulations and training methods for learning models of stochastic dynamics based on observation of trajectory samples. We develop approaches using discriminators based on Maximum Mean Discrepancy (MMD), training protocols using conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. The adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

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• Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks
• Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models

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