학술행사
- 발표자 김연응 교수(서울과학기술대학교)
- 개최일시 2025-03-27 15:00-17:00
- 장소 국가수리과학연구소 산업수학혁신센터(판교)
유튜브 스트리밍 예정입니다.
- 일시: 2025년 3월 27일(목), 15:00-17:00
- 장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소 / 무료주차는 2시간 지원됩니다.
- 발표자: 김연응 교수(서울과학기술대학교)
- 주제: Hamilton–Jacobi Based Policy-Iteration via Deep Operator LearningThe framework of deep operator network (DeepONet) has been widely exploited thanks to its capability of solving high dimensional partial differential equations. In this talk, we incorporate DeepONet with a recently developed policy iteration scheme to numerically solve optimal control problems and the corresponding Hamilton–Jacobi–Bellman (HJB) equations. A notable feature of our approach is that once the neural network is trained, the solution to the optimal control problem and HJB equations with different terminal functions can be inferred quickly thanks to the unique feature of operator learning. Furthermore, a quantitative analysis of the accuracy of the algorithm is carried out via comparison principles of viscosity solutions. The effectiveness of the method is verified with various examples, including 10-dimensional linear quadratic regulator problems (LQRs). This talk is based on the joint work with Jae Yong Lee at Chung-Ang University.
유튜브 스트리밍 예정입니다.
- 일시: 2025년 3월 27일(목), 15:00-17:00
- 장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소 / 무료주차는 2시간 지원됩니다.
- 발표자: 김연응 교수(서울과학기술대학교)
- 주제: Hamilton–Jacobi Based Policy-Iteration via Deep Operator LearningThe framework of deep operator network (DeepONet) has been widely exploited thanks to its capability of solving high dimensional partial differential equations. In this talk, we incorporate DeepONet with a recently developed policy iteration scheme to numerically solve optimal control problems and the corresponding Hamilton–Jacobi–Bellman (HJB) equations. A notable feature of our approach is that once the neural network is trained, the solution to the optimal control problem and HJB equations with different terminal functions can be inferred quickly thanks to the unique feature of operator learning. Furthermore, a quantitative analysis of the accuracy of the algorithm is carried out via comparison principles of viscosity solutions. The effectiveness of the method is verified with various examples, including 10-dimensional linear quadratic regulator problems (LQRs). This talk is based on the joint work with Jae Yong Lee at Chung-Ang University.
