{"id":47141,"date":"2023-12-20T15:02:45","date_gmt":"2023-12-20T20:02:45","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=47141"},"modified":"2024-08-21T13:54:52","modified_gmt":"2024-08-21T17:54:52","slug":"ams-weekly-seminar-professor-eric-vanden-eijnden","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-professor-eric-vanden-eijnden\/","title":{"rendered":"AMS Weekly Seminar | Professor Eric Vanden-Eijnden"},"content":{"rendered":"

Location:<\/strong>Gilman 132<\/p>\n

When:<\/strong> February 8th at 1:30 p.m.<\/p>\n

Title:<\/strong> Scientific Computing in the Age of Generative AI<\/span><\/p>\n

Abstract:\u00a0<\/strong>Generative models based on dynamical transport have recently led to significant advances in unsupervised learning. At mathematical level, these\u00a0models\u00a0are primarily designed around the construction of a map between two probability distributions that transform samples from the first into samples from the second. \u00a0While these methods were first introduced in the context of image generation, they have found a wide range of applications, including in scientific computing where they offer interesting ways to reconsider complex problems once thought intractable because of the curse of dimensionality. In this talk, I will discuss the mathematical underpinning of generative models based on flows and diffusions, and show how a better understanding of their inner workings can help improve their design. These results indicate how to\u00a0structure the transport to best reach complex target distributions while maintaining computational efficiency, both at learning and sampling stages.\u00a0\u00a0I will also discuss applications of generative AI in scientific computing, in particular in the context of Monte Carlo sampling,\u00a0with applications to the statistical mechanics and Bayesian inference, as well as probabilistic forecasting, with application to fluid dynamics and atmosphere\/ocean science.<\/p>\n

Bio:<\/strong>\u00a0Eric Vanden-Eijnden (\u00a0https:\/\/wp.nyu.edu\/courantinstituteofmathematicalsciences-eve2\/<\/a>\u00a0) is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University.\u00a0 His research\u00a0focuses on the mathematical and computational aspects of statistical mechanics, with applications\u00a0to complex dynamical systems arising in molecular dynamics, \u00a0materials science, atmosphere-ocean\u00a0science, fluids dynamics, and neural networks. He is also interested in the\u00a0mathematical foundations of machine learning (ML) and the\u00a0applications of ML in scientific computing. He is known for the development and analysis of multiscale numerical methods for systems whose dynamics span a wide\u00a0range of spatio-temporal scales. He is the winner of the\u00a0Germund Dahlquist Prize and the\u00a0J.D. Crawford Prize, and a recipient of the Vannevar Bush Faculty Fellowship. \u00a0He was a plenary speaker at the 2015 International Congress of Industrial and Applied Mathematics (ICIAM) in Beijing and an invited speaker at the 2022\u00a0International Congress of Mathematics (ICM).<\/p>\n

Zoom link: <\/strong>https:\/\/wse.zoom.us\/j\/94601022340<\/p>\n","protected":false},"excerpt":{"rendered":"

Location:Gilman 132 When: February 8th at 1:30 p.m. Title: Scientific Computing in the Age of Generative AI Abstract:\u00a0Generative models based on dynamical transport have recently led to significant advances 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