Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

2019/10/03 by Rushil Anirudh, Anirudh, Rushil, Jayaraman J. Thiagarajan +7 · 1 citation
Engineering · Physics and Astronomy · #Computational Physics (physics.comp-ph) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Laser-Plasma Interactions and Diagnostics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Nuclear Engineering Thermal-Hydraulics

paper · pdf · doi:10.48550/arxiv.1910.01666

openalex publication_date 2019/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion. However, a common challenge is to verify the scientific plausibility or validity of outputs predicted by a neural network. This work advocates the use of known scientific constraints as a lens into evaluating, exploring, and understanding such predictions for the problem of inertial confinement fusion.

Citations

• Wasserstein Auto-Encoders
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Cited by

• 2022 Review of Data-Driven Plasma Science

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