# 유튜브 스트리밍 예정입니다. 1. 일시: 2024년 10월 29일(화), 10:30-12:30 2. 장소: 판교 테크노밸리 산업수학혁신센터 세미나실 - 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소 - 무료주차는 2시간 지원됩니다. 3. 발표자: 김혜현 교수(경희대학교) 4. 주요내용: Partitioned neural networks for partial differential equations In this talk, I will give a brief introduction to neural network approximation to partial differential equations (PDEs). To deal with error and performance issues in the neural network approximation, partitioned neural networks (PNNs) can be used as a solution surrogate. PNNs are formed based on the partition of the problem domain and each network in PNNs approximates the PDE solution in each small subdomain. The use of PNNs allows more flexible local network and hyperparameter settings, and more efficient parameter training by parallel computation. In the second part of my talk, I will present iterative algorithms for PNNs, that can utilize the partitioned network structure for the parallel computation. Numerical results will be presented for test examples to show the performance and scalability of the proposed iterative algorithms.