This presentation will outline recent evolution of AI methodologies, focusing on the emergence of Diffusion Models of AI inspired by non-equilibrium statistical mechanics, Transformers, and Reinforcement Learning. These innovations are revolutionizing our approach to reduced, Lagrangian turbulence modeling and are instrumental in formulating and solving new challenges, such as swimming navigation in chaotic environments.
More generally, attendees will gain insights into the synergy between AI and natural sciences and understand how this symbiosis is shaping the future of scientific research. This comprehensive vision is relevant to theoretical physicists, applied mathematicians, and computer scientists alike.
