[20210528 : Colloquium]

Data-driven modeling for stochastic systems with machine learning



1. 일시 2021년 5월 28일 (금) 11:00-12:00

2. 장소 : 아산이학관 526호 및 Zoom을 이용한 실시간 온라인 강연 동시 진행

- Zoom링크 : 

  https://korea-ac-kr.zoom.us/j/85782653817?pwd=U3d5c2NCWHlCamJlMmErVXNCSUdCZz09

회의ID : 857 8265 3817, 암호 : qm4DVSzDP.

3. 연사 : 최민석 교수 (포항공대 수학과)

4. 제목 Data-driven modeling for stochastic systems with machine learning

5. 초록 Models of physical systems typically involve uncertainty in the input data such as those associated with coefficients initial or boundary conditions, geometry, etc. Estimating the propagation of this uncertainty into model output predictions is crucial to provide more insight to the true physics and produce predictions with high fidelity. This often leads to solve partial differential equations with many parameters. We discuss machine learning based algorithms to solve parametric PDEs and present numerical examples to demonstrate the effectiveness of the proposed methods.