计算与应用讨论班

报告题目：Learning to Solve PDEs: Scientific Machine Learning from Principles to Practice

报 告 人：Minseok Choi 副教授（韩国浦项科技大学）

时  间：2026年4月14日（星期二），下午14:15-16:30

地  点：海纳苑2幢102

摘  要：Scientific Machine Learning (SML) is rapidly emerging as a powerful paradigm for addressing complex problems in science and engineering by integrating machine learning with real-world data and the fundamental laws of physics. This talk will provide a concise overview of the core concepts and algorithmic foundations of SML. In particular, it will introduce methodologies such as Physics-Informed Neural Networks (PINNs), which incorporate physical constraints directly into the learning process, and Operator Learning, which seeks to learn mappings between function spaces and thereby enables fast and efficient prediction of system responses under varying input conditions. The talk will also discuss recent developments aimed at overcoming key limitations of early PINN and operator learning approaches, including issues of long-time integration, data efficiency, generalization, and computational stability. Finally, several examples will be presented to illustrate how SML can lead to innovative and effective solutions in practical applications, often providing substantial speed-ups over traditional numerical simulations.

报告人简介：Minseok Choi(崔珉硕) received his B.S. and M.S. degrees from Seoul National University, South Korea, and his Ph.D. in Applied Mathematics from Brown University, USA. After completing his Ph.D., he worked as a Postdoctoral Researcher at Princeton University before joining Pohang University of Science and Technology (POSTECH), where he is currently an Associate Professor of Mathematics. He also serves as Vice President of the East Asia Section of the Society for Industrial and Applied Mathematics (EASIAM). His research interests include scientific machine learning, AI for science, and uncertainty quantification.