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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.

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🌟 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 | Youngsoo Choi (LLNL) - LaSDI: Latent space dynamics identification

MINDS SEMINAR

period : 2024-09-24 ~ 2024-09-24

time : 11:00:00 ~ 12:00:00

개최 장소 : Online streaming (Zoom)

Topic : LaSDI: Latent space dynamics identification

개요

Date	2024-09-24 ~ 2024-09-24	Time	11:00:00 ~ 12:00:00
Speaker	Youngsoo Choi	Affiliation	LLNL (Lawrence Livermore National Lab)
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	LaSDI: Latent space dynamics identification
Contents	Many computational models for physical systems have been developed to expedite scientific discovery, which would otherwise be impeded by the lengthy nature of traditional, non-computational experimentation (e.g., observation, problem identification, hypothesis formulation, experimentation, data collection, analysis, conclusion, and theory development). However, as these physical systems grow more complex, the computational models themselves can become prohibitively time-consuming. To address this challenge, we introduce a framework called Latent Space Dynamics Identification (LaSDI), which transforms complex, high-dimensional computational domains into reduced, succinct coordinate systems—a sophisticated change of variables that preserves essential dynamics. LaSDI offers significant potential for extension to other innovative, data-driven algorithms. It is an interpretable, data-driven framework composed of three core steps: compression, dynamics identification, and prediction [3, 4, 7, 6, 8]. In the compression phase, high-dimensional data is reduced to a more manageable form, facilitating the construction of an interpretable model. The dynamics identificationphase then derives a model, typically expressed as parameterized differential equations, that accurately captures the behavior of the reduced latent space. Finally, in the prediction phase, these differential equations are solved within the reduced space for new parameter sets, with the resulting solutions projected back into the full space. One of the key advantages of LaSDI is its computational efficiency, as the prediction phase operates entirely in the reduced space, bypassing the need for the full-order model. The LaSDI framework supports various identification methods, including fixed forms like dynamic mode decomposition [1] and thermodynamics-based LaSDI [6], regression methods such as sparse identification of nonlinear dynamics(SINDy) and weak SINDy, as well as physics-driven approaches like projection-based reduced order models[5, 2]. The LaSDI family has demonstrated substantial success in accelerating various physics problems, achieving up to 1000x speed-ups in fields such as kinetic plasma simulations [8], pore collapse phenomena [1], and computational fluid dynamics [2,5,6].