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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 | Jun Sur Park(KAIST) - Advancing model reduction techniques: deep learning approaches for homogenization and reduced order modeling

MINDS SEMINAR

period : 2023-05-23 ~ 2023-05-23

time : 17:00:00 ~ 18:00

개최 장소 : Math Bldg 100&Online streaming (Zoom)

Topic : Advancing model reduction techniques: deep learning approaches for homogenization and reduced order modeling

개요

Date	2023-05-23 ~ 2023-05-23	Time	17:00:00 ~ 18:00
Speaker	Jun Sur Park	Affiliation	KAIST
Place	Math Bldg 100&Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Advancing model reduction techniques: deep learning approaches for homogenization and reduced order modeling
Contents	This presentation introduces the application of deep learning approaches in two model reduction techniques. The first part focuses on homogenization of multiscale elliptic equations. Multiscale equations with scale separation are often approximated by the corresponding homogenized equations with slowly varying homogenized coefficients (the G-limit). We develop a physics-informed neural networks (PINNs) algorithm to estimate the G-limits from the multiscale solution data. Unlike the traditional approaches, our approach does not rely on the periodicity assumption or the known multiscale coefficient during the learning stage. The second part of the presentation introduces a reduced order modeling for parameterized dynamical systems. Our proposed algorithm leverages autoencoders to capture the latent representation of high-dimensional full-order model data. Additionally, we employ the GENERIC formalism informed neural networks (GFINNs) to learn the dynamics of the latent variables. By training these neural networks simultaneously, we achieve efficient and accurate reduced order models for parameterized dynamical systems.