Welcome to MINDS!

Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

News

🌟 DACON Ranker Special Lecture: Winning Strategies for AI Competitions 🌟 2023.11.30

3nd POSTECH&Peking SIAM Student Chapter Joint Conference 2023.11.01

2023 PSSC Summer Camp 2023.10.04

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고]-상시모집 2023.07.25

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.07.14

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구교수 채용 공고] 2023.06.12

Seminar | Joint seminar for probability and mathematical biology 2023.05.02

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.02.15

Upcoming Events

Schedule

MINDS SEMINAR

MINDS Seminar Series | Sun Woo Hwang (POSTECH MINDS) - Neural network approach to data-driven estimation of the Patlak-Keller-Segel model

MINDS SEMINAR

period : 2022-03-15 ~ 2022-03-15

time : 17:00:00 ~ 18:00

개최 장소 : Online streaming (Zoom)

Topic : Neural network approach to data-driven estimation of the Patlak-Keller-Segel model

개요

Date	2022-03-15 ~ 2022-03-15	Time	17:00:00 ~ 18:00
Speaker	Sun Woo Hwang	Affiliation	POSTECH MINDS
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Neural network approach to data-driven estimation of the Patlak-Keller-Segel model
Contents	We consider the mathematical model of chemotaxis introduced by Patlak, Keller, and Segel. Aggregation and progression waves are present everywhere in the population dynamics of chemotactic cells. Aggregation originates from the chemotaxis of mobile cells, where cells are attracted to migrate to higher concentrations of the chemical signal region produced by themselves. The neural network can be used to find the approximate solution of the PDE. We proved that the error, the difference between the real value and the predicted value, is bound to a constant multiple of the loss we are learning. Also, the neural network approximation can be easily applied to the inverse problem. It was confirmed that even when the coefficient of the PDE equation was unknown, prediction with high accuracy was achieved.