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 | Jihun Han (Dartmouth College) - Collaboration between PDEs and machine learning: solving PDEs and learning dynamical systems using neural networks

MINDS SEMINAR

period : 2024-04-23 ~ 2024-04-23

time : 10:00:00 ~ 11:00:00

개최 장소 : Online streaming (Zoom)

Topic : Collaboration between PDEs and machine learning: solving PDEs and learning dynamical systems using neural networks

개요

Date	2024-04-23 ~ 2024-04-23	Time	10:00:00 ~ 11:00:00
Speaker	Jihun Han	Affiliation	Dartmouth College
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Collaboration between PDEs and machine learning: solving PDEs and learning dynamical systems using neural networks
Contents	This talk has two main parts. Firstly, I will discuss a stochastic approach for solving PDEs: the derivative-free loss method (DFLM). I will cover its analysis and highlight its advantages in addressing multiscale problems and perforated domain problems in a non-intrusive manner. In the second part, I will introduce a method for learning in-between imagery dynamics. This method involves integrating PDE models within the latent spaces to enhance learning. Notably, this method exhibits robustness in capturing intricate dynamics, such as rotation and outflow, which are challenging for existing state-of-the-art methods of optimal transport.