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?
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?
|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 · 2021-09-24 10:06 · Views 168