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Scientific Deep Learning for Forward and Inverse Modeling of Spatiotemporal Physics

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posted on 2023-04-04, 00:00 authored by Han Gao

This dissertation presents several machine learning frameworks that can solve challenging forward and inverse problems of physical spatiotemporal systems. To achieve this, modern deep learning models for complex systems were developed by integrating machine learning, numerical methods, and probabilistic modeling. In particular, the thesis focuses on physics-informed machine learning, data-driven modeling, which involve convolutional neural network (CNN), graph neural network (GNN), transformer, and deep probabilistic model. Taken together, the frameworks presented in this thesis offer useful insight into the impact of modern deep learning models on solving challenging spatiotemporal systems in physics.

History

Date Modified

2023-04-12

Defense Date

2023-03-30

CIP Code

  • 14.1901

Research Director(s)

Jian-Xun Wang

Degree

  • Doctor of Philosophy

Degree Level

  • Doctoral Dissertation

Alternate Identifier

1375584542

OCLC Number

1375584542

Additional Groups

  • Aerospace and Mechanical Engineering

Program Name

  • Aerospace and Mechanical Engineering

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