Schedule

MINDS SEMINAR

MINDS Seminar Series | Seungjoon Lee (San Jose State University) - Data-driven ODEs/PDEs from Microsopic Data: What and How Can We Learn Them from Data?

MINDS SEMINAR
period : 2021-11-09 ~ 2021-11-09
time : 10:00:00 ~ 11:00:00
개최 장소 : Online streaming (Zoom)
Topic : Data-driven ODEs/PDEs from Microsopic Data: What and How Can We Learn Them from Data?
개요
Date 2021-11-09 ~ 2021-11-09 Time 10:00:00 ~ 11:00:00
Speaker Seungjoon Lee Affiliation San Jose State University
Place Online streaming (Zoom) Streaming link ID : 688 896 1076 / PW : 54321
Topic Data-driven ODEs/PDEs from Microsopic Data: What and How Can We Learn Them from Data?
Contents Traditionally, mathematical models have been widely used to understand complex behaviors or dynamics in science and engineering, even economics and social science. However, identifying a proper mathematical system for certain phenomena requires deep prior knowledge about related theories and advanced mathematics; development has been lagging behind. Nowadays, thanks to the advances in machine learning techniques and data-driven modeling, we are able to effectively identify mathematical models (e.g., ordinary/partial differential equations (ODE/PDE)) from data directly without (or a little) prior knowledge. In this talk, Dr. Lee will present some machine learning techniques of data-driven ODE/PDEs from microscopic data with various examples. Through these examples, I illustrate the concept of the black-box model and the (partially physics informed) gray box model to identify/explain model ODE/PDE. Moreover, he will introduce the correction model of the existing theory-grounded models to enhance prediction accuracy. Such data-driven models can be applied to various research topics from science/engineering to economics. Hence, this presentation will suggest a new direction for a future curriculum for data-driven modeling in our department (and STEM field).
MinDS MinDS · 2021-09-24 10:06 · Views 224