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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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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.
MinDS MinDS · 2023-05-17 08:33 · Views 463

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