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 | Yoonsang Lee (Dartmouth college) - Sampling error mitigation through spectrum smoothing in ensemble data assimilation

MINDS SEMINAR

period : 2024-09-09 ~ 2024-09-09

time : 17:00:00 ~ 18:00

개최 장소 : Math Bldg 104 & Online streaming (Zoom)

Topic : Sampling error mitigation through spectrum smoothing in ensemble data assimilation

개요

Date	2024-09-09 ~ 2024-09-09	Time	17:00:00 ~ 18:00
Speaker	Yoonsang Lee	Affiliation	Dartmouth college
Place	Math Bldg 104 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Sampling error mitigation through spectrum smoothing in ensemble data assimilation
Contents	In data assimilation, an ensemble provides a nonintrusive way to evolve a probability density described by a nonlinear prediction model. Although a large ensemble size is required for statis- tical accuracy, the ensemble size is typically limited to a small number due to the computational cost of running the prediction model, which leads to a sampling error. Several methods, such as localization, exist to mitigate the sampling error, often requiring problem-dependent fine-tuning and design. However, many of such methods are applied homogeneously in the physical domain, which is lack of adaptivity. This work introduces another sampling error mitigation method using a smoothness constraint in the Fourier space, and accordingly achieve the inhomogeneous mitigation effect in the physical space. In particular, this work smoothes out the spectrum of the system to increase the stability and accuracy even under a small ensemble size. The efficacy of the new idea is validated through a suite of stringent test problems, including Lorenz 96.