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 | JaEun Ku (Oklahoma State University) - Dual finite element methods for singularly perturbed reaction-diffusion problems.

MINDS SEMINAR

period : 2023-10-17 ~ 2023-10-17

time : 17:00:00 ~ 18:00

개최 장소 : Math Bldg 404 & Online streaming (Zoom)

Topic : Dual finite element methods for singularly perturbed reaction-diffusion problems.

개요

Date	2023-10-17 ~ 2023-10-17	Time	17:00:00 ~ 18:00
Speaker	JaEun Ku	Affiliation	Oklahoma State University
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Dual finite element methods for singularly perturbed reaction-diffusion problems.
Contents	Recently, dual finite element methods are developed for accurate and efficient approximations of solutions of singularly pertubed reaction-diffusion problems. The methods only approximate the dual variables without approximating the primary variable. An approximation for the primary variable can be recovered through a simple local $L_2$ projection. The methods provide a competitive alternative to the other existing numerical methods. For examples, the approximation solution for the primary variable does not show numerical oscillation, which is observed in the standard Galerkin methods. The first part of this talk introduces the dual finite element methods, and the second part presents various a priori and a posteriori error estimates including guaranteed upper bounds for dual approximation.