Non Equilibrium

Non-equilibrium physics explores systems far from thermodynamic equilibrium, focusing on understanding their dynamics and emergent properties. Current research heavily utilizes machine learning, particularly deep learning architectures like diffusion models, transformers, and graph neural networks, to analyze and model these complex systems, often drawing parallels with stochastic thermodynamics and optimal transport theory. This interdisciplinary approach is yielding insights into diverse fields, from the fundamental workings of neural networks and generative models to the optimization of quantum thermal machines and the prediction of emergent behavior in active matter systems. The resulting advancements have significant implications for both theoretical understanding of complex systems and practical applications in areas like materials science, drug discovery, and traffic management.