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Explore how generative AI methods like diffusion models and flow matching can revolutionize physics simulations in this seminar by Prof. Nils Thürey from TU Munich. Learn how these advanced AI techniques move beyond traditional single-output predictions to capture full probability distributions, enabling multiple sample generation and comprehensive exploration of possible outcomes. Discover the integration of generative AI with conventional numerical methods to create powerful inverse solvers that deliver both speed and accuracy. Understand how probabilistic models can quantify uncertainty and provide deeper insights into the behavior of complex physical systems. The presentation demonstrates practical applications of data-driven simulators that leverage the probabilistic nature of generative models to enhance traditional computational physics approaches. Gain insights into cutting-edge research at the intersection of artificial intelligence and computational physics, with particular focus on how uncertainty quantification can improve our understanding of simulated systems.
