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Accession Number:

AD1208932

Title:

Machine Learning for PAC1D and SESE

Author(s):

Author Organization(s):

Report Date:

2023-04-03

Abstract:

This document outlines various machine learning approaches that were taken in an effort tosurrogate numerical models Python Ablation Code 1-Dimension (PAC1D) and Scalable EffectsSimulation Environment (SESE)[1], [2], with the ultimate objective of discovering the mostefficient method for approximating SESE with sparse data utilization. The methods exploredinclude; physics-informed neural networks, deep galerkin method, deep mixed residual methods,operator network, deep operator network, fourier neural operator, physics-informed fourier neuraloperator, and physics-informed kernal neural operator. Many of the methods showed strengths andweaknesses in their performance, with the physics-informed kernal neural operator showing themost potential for approximating SESEs behavior.

Pages:

44

File Size:

1.70MB

Descriptors:

Communities of Interest:

Distribution Statement:

Approved For Public Release

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