MINDS Seminar Series | Jae Won Choi (University of Texas at Dallas) - Bridging Physics and Graph Intelligence: Physics-Informed Machine Learning with Graph Neural Networks for Dynamic PDE Modeling

MINDS SEMINAR

period : 2025-04-15 ~ 2025-04-15

time : 10:00:00 ~ 11:00:00

개최 장소 : Online streaming (Zoom)

Topic : Bridging Physics and Graph Intelligence: Physics-Informed Machine Learning with Graph Neural Networks for Dynamic PDE Modeling

개요

Date	2025-04-15 ~ 2025-04-15	Time	10:00:00 ~ 11:00:00
Speaker	Jae Won Choi	Affiliation	University of Texas at Dallas
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Bridging Physics and Graph Intelligence: Physics-Informed Machine Learning with Graph Neural Networks for Dynamic PDE Modeling
Contents	Many real-world systems from weather dynamics to wildfire smoke dispersion are governed by complex partial differential equations (PDEs), yet traditional numerical solvers often struggle in data-sparse, irregular, and topologically complex settings. Physics-Informed Machine Learning (PIML) has emerged as a transformative paradigm that embeds physical laws into machine learning models to ensure consistency, efficiency, and generalizability. In this talk, I will explore the evolution of PIML, beginning with the foundational framework of Neural Ordinary Differential Equations (Neural ODEs), which laid the groundwork for modeling continuous-time dynamics through differentiable solvers. I will then trace key developments in the field highlighting limitations of early approaches such as PINNs and discrete-grid PDE solvers and present how Graph Neural Networks (GNNs) enable the modeling of PDEs over unstructured spatial domains.

MinDS · 2025-04-08 08:56 · Views 254

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