Fields, Strings, and Deep Learning
Progress in deep learning has traditionally involved experimental data, but in recent years it has impacted our understanding of formal structures arising in theoretical high energy physics and pure mathematics, via both theoretical and applied deep learning. This conference will bring together high energy theorists, mathematicians, and computer scientists across a broad variety of topics at the interface of these fields. Featured topics include the interface of neural network theory with quantum field theory, lattice field theory, conformal field theory, and the renormalization group; theoretical physics for AI, including equivariant, diffusion, and other generative models; ML for pure mathematics, including knot theory and special holonomy metrics, and deep learning for applications in string theory and holography.
Organizers
Miranda Cheng, Michael Douglas, Jim Halverson, Fabian Ruehle
