Presentation
G-LED: Generative AI for Learning the Effective Dynamics of High-dimensional, Complex Systems
DescriptionWe present novel generative models for accelerating simulations of high-dimensional systems through learning and evolving their effective dynamics. In our Generative Learning of Effective Dynamics (G-LED) framework, instances of high dimensional data are down sampled to a lower dimensional manifold that is evolved through an auto-regressive attention mechanism. Subsequently Bayesian diffusion models are employed, that map this low-dimensional manifold onto its corresponding high-dimensional space. These diffusion models operate simultaneously on batches of data and can incorporate physical constraints using the concept of virtual observables and gradient guidance. We demonstrate unprecedented capabilities in capturing the evolution of benchmark models such as Kuramoto-Sivashinsky as well as simulations of 3D turbulent channel flows.