수학과

안녕하세요. 수학과입니다.

5/1 (목)에 예정된 수학과 colloquium에 대해 안내 드립니다. 

많은 참여와 관심을 부탁 드립니다.

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일시: 5월 1일(목) 16:30~17:30

장소: 팔달관 621호

연사: 이재용 교수님 (중앙대학교 AI학과)

Title: Two approaches using Deep Learning to solve Partial Differential Equations

Abstract: Many differential equations and partial differential equations (PDEs) are being studied to model physical phenomena in nature with mathematical expressions. Recently, new numerical approaches using machine learning and deep learning have been actively studied. There are two mainstream deep learning approaches to approximate solutions to the PDEs, i.e., using neural networks directly to parametrize the solution to the PDE and learning operators from the parameters of the PDEs to their solutions. As the first direction, Physics-Informed Neural Network was introduced in (Raissi, Perdikaris, and Karniadakis 2019), which learns the neural network parameters to minimize the PDE residuals in the least-squares sense. On the other side, operator learning using neural networks has been studied to approximate a PDE solution operator, which is nonlinear and complex in general. In this talk, I will introduce these two ways to approximate the solution of PDE and my research related to them.

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