Скачать с ютуб Dr. Paris Perdikaris -- Supervised and physics-informed learning in function spaces в хорошем качестве

Dr. Paris Perdikaris -- Supervised and physics-informed learning in function spaces

8 September, 2022 15:30 (local Swedish time) Supervised and physics-informed learning in function spaces Paris Perdikaris (University of Pennsylvania) Abstract: While the great success of modern deep learning lies in its ability to approximate maps between finite-dimensional vector spaces, many tasks in science and engineering involve continuous measurements that are functional in nature. For example, in climate modeling one might wish to predict the pressure field over the earth from measurements of the surface air temperature field. The goal is then to learn an operator, between the space of temperature functions to the space of pressure functions. In recent years, operator learning techniques have emerged as a powerful tool for supervised learning in infinite-dimensional function spaces. In this talk we will provide an introduction to this topic, present a general approximation framework for operators, and demonstrate how one can construct deep learning models that can handle functional data. We will see how such tools can help us build neural ODE and PDE solvers that can be trained even in the absence of labeled data, and enable the fast prediction of continuous spatio-temporal fields up to three orders of magnitude faster compared to conventional numerical solvers. We will also discuss key open questions related to generalization, data-efficiency and inductive bias, the resolution of which is critical for the success of AI in science and engineering. Paris Perdikaris is an Assistant Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. He received his PhD in Applied Mathematics at Brown University in 2015, and, prior to joining Penn in 2018, he was a postdoctoral researcher at the department of Mechanical Engineering at the Massachusetts Institute of Technology. His current research interests include physics-informed machine learning, uncertainty quantification, and engineering design optimization. His work and service has received several distinctions including the DOE Early Career Award (2018), the AFOSR Young Investigator Award (2019), the Ford Motor Company Award for Faculty Advising (2020), the SIAG/CSE Early Career Prize (2021), and the Scialog Fellowship (2021). Visit the Chalmers AI4Science seminar series: https://psolsson.github.io/AI4Science...