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Machine Learning for PAC1D and SESE

Abstract
This document outlines various machine learning approaches that were taken in an effort to surrogate numerical models Python Ablation Code 1-Dimension (PAC1D) and Scalable Effects Simulation Environment (SESE), with the ultimate objective of discovering the most efficient method for approximating SESE with sparse data utilization. The methods explored include; physics-informed neural networks, deep Galerkin method, deep mixed residual methods, operator network, deep operator network, Fourier neural operator, physics-informed Fourier neural operator, and physics-informed kernel neural operator. Many of the methods showed strengths and weaknesses in their performance, with the physics-informed kernel neural operator showing the most potential for approximating SESE’s behavior.
Type
Report
Type of thesis
Series
Citation
Date
2023-04-03
Publisher
U.S. Air Force Research Laboratory
Degree
Supervisors
Rights
© 2023 The Authors.