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 | Jae Hyuk Lim (Jeonbuk University) - Towards Enhanced Physics-Informed Machine Learning for Industrial Applications

MINDS SEMINAR

period : 2024-11-26 ~ 2024-11-26

time : 15:30:00 ~ 16:30:00

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

Topic : Towards Enhanced Physics-Informed Machine Learning for Industrial Applications

개요

Date	2024-11-26 ~ 2024-11-26	Time	15:30:00 ~ 16:30:00
Speaker	Jae Hyuk Lim	Affiliation	Jeonbuk University
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Towards Enhanced Physics-Informed Machine Learning for Industrial Applications
Contents	Physics-Informed Machine Learning (PIML) has emerged as a transformative approach, integrating physical laws with data-driven methods to enhance prediction accuracy and interpretability in industrial applications. This study presents advancements in PIML methodologies, focusing on three key aspects: improving convergence, scaling to large models, and enabling model discovery. By employing novel optimization techniques and adaptive training schemes, we achieve enhanced convergence, even in scenarios with complex, nonlinear physical constraints. Furthermore, the proposed framework demonstrates scalability, leveraging parallel computing and efficient architecture design to accommodate large-scale industrial systems. Finally, we explore model discovery, utilizing sparse regression and neural architecture search to uncover governing equations and hidden dynamics directly from data. These enhancements make PIML more robust and versatile, enabling its application across diverse industries such as energy systems, aerospace engineering, and smart manufacturing. This work contributes to bridging the gap between theoretical developments and practical implementations, paving the way for next-generation industrial solutions.