AI for Stochastic Dynamics: From Theoretical Foundations to Scientific Applications

Stochastic dynamics provides a mathematical language for modeling systems driven by intrinsic randomness, with applications across fluids, climate, finance, biology, materials, and molecular science. It is also becoming central to modern AI through diffusion models, neural SDEs, stochastic neural operators, probabilistic forecasting, and learning-based control. Despite this progress, there remains a gap between the theoretical foundations of stochastic dynamics and their use in scientific machine learning applications. Key challenges include defining learning objectives aligned with stochastic quantities of interest, incorporating stochastic structure into models and solvers, and validating whether learned systems reproduce the behavior required by the application. This workshop aims to bridge theoretical foundations and scientific applications by bringing together researchers from stochastic analysis, applied probability, numerical simulation, machine learning, and AI-driven scientific domains. Through invited talks, contributed presentations, posters, and open discussion, the workshop will build a focused community around principled, reliable, and scientifically meaningful AI for stochastic dynamics.