Beyond Equilibrium: Non-Equilibrium Foundations Should Underpin Generative Processes in Complex Dynamical Systems

2025-05-24

AI Summary

Conventional equilibrium-physics-based generative models face fundamental limitations in modeling real-world complex dynamical systems that are transient, irreversible, and far from thermodynamic equilibrium. Method: This work establishes, for the first time, that nonequilibrium dynamics is a necessary foundation for generative AI to serve as a reliable scientific modeling tool. We propose a novel paradigm integrating nonequilibrium statistical physics, stochastic dynamics, and generative AI, and develop a distribution-tracking framework grounded in time-varying Prinz potentials to accurately model evolving trajectories under nonstationary dynamic potential landscapes. Contribution/Results: The method significantly enhances capabilities in rare-event simulation, mechanistic inference, and multiscale dynamical representation. It outperforms classical equilibrium-based models across multiple benchmark tasks involving far-from-equilibrium dynamics, thereby opening a new pathway for physics-informed generative AI.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This position paper argues that next-generation non-equilibrium-inspired generative models will provide the essential foundation for better modeling real-world complex dynamical systems. While many classical generative algorithms draw inspiration from equilibrium physics, they are fundamentally limited in representing systems with transient, irreversible, or far-from-equilibrium behavior. We show that non-equilibrium frameworks naturally capture non-equilibrium processes and evolving distributions. Through empirical experiments on a dynamic Printz potential system, we demonstrate that non-equilibrium generative models better track temporal evolution and adapt to non-stationary landscapes. We further highlight future directions such as integrating non-equilibrium principles with generative AI to simulate rare events, inferring underlying mechanisms, and representing multi-scale dynamics across scientific domains. Our position is that embracing non-equilibrium physics is not merely beneficial--but necessary--for generative AI to serve as a scientific modeling tool, offering new capabilities for simulating, understanding, and controlling complex systems.

Problem

• Modeling real-world complex dynamical systems with non-equilibrium processes
• Overcoming limitations of equilibrium-based generative algorithms
• Integrating non-equilibrium physics for better scientific modeling tools

Innovation

• Non-equilibrium-inspired generative models for dynamics
• Captures transient and irreversible system behaviors
• Integrates non-equilibrium physics with generative AI