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)[1], [2], 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 kernal neural operator. Many of the methods showed strengths and weaknesses in their performance, with the physics-informed kernal neural operator showing the most potential for approximating SESE's behavior.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 03, 2023
- Accession Number
- AD1208932
Entities
People
- Brett A. Bowman
- Chad A. Oian
- Jason A. Kurz
- Matthew G. Seman
- Taufiquar Khan