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Modern Deep Learning for Modeling Dynamical Systems

thesis
posted on 2022-02-21, 00:00 authored by Nicholas Geneva

Advances in deep learning have made constructing, training and deploying deep neural networks more accessible than ever before. Due to their flexibility and predictive accuracy, neural networks have ushered in a new wave of data-driven and data-free modeling for physical phenomena. With several key research breakthroughs in the deep learning field, modern deep learning architectures are now more accurate and generalizable facilitating improved physics-informed models. This dissertation explores the use of several different deep learning approaches for learning physical dynamics including Bayesian neural networks, generative models, physics-constrained learning and self-attention. By leveraging these recent deep neural network advancements and probabilistic frameworks, powerful deep learning surrogates of physical systems can predict complex mutli-scale features.

History

Date Modified

2022-03-02

Defense Date

2021-12-15

CIP Code

  • 14.1901

Research Director(s)

Nicholas Zabaras

Degree

  • Doctor of Philosophy

Degree Level

  • Doctoral Dissertation

Alternate Identifier

1300757397

Library Record

6168231

OCLC Number

1300757397

Program Name

  • Aerospace and Mechanical Engineering

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