Workshop organized by: D. Gupta and T. Pham
Many physical and biological systems operate far from equilibrium, continuously consuming energy and exhibiting collective behavior driven by stochastic dynamics. Prominent examples include active matter and biological molecular machines, where dissipation, fluctuations, and broken detailed balance are essential to function. Recent advances in non-equilibrium statistical mechanics, notably fluctuation relations, thermodynamic uncertainty relations, and stochastic optimal control, have provided powerful tools to analyze such systems. This workshop aims to provide a panoramic overview of interdisciplinary applications of statistical physics, with a focus on active matter, biological machines, and complex systems. A complementary theme is the growing interface between statistical physics and machine learning, including the use of learning algorithms to infer nonequilibrium thermodynamic properties and to design optimal control strategies for stochastic systems, as well as the physics of neural networks viewed as high-dimensional interacting systems with emergent collective behavior. The meeting aims to bring together PhD students, postdoctoral researchers, and experts from a broad range of fields who are willing to exchange experience in addressing both fundamental and applied problems, and bridging cross-disciplinary approaches, paving the way toward understanding a wide range of complex systems.