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Machine learning-driven image synthesis and analysis applications for inertial confinement fusion (invited).

Bradley T Wolfe¹, Pinghan Chu¹, Nga T T Nguyen-Fotiadis¹

¹Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.

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|December 17, 2024

Summary

Deep neural networks enable integrated analysis of fusion diagnostics data. This approach aids in optimizing inertial confinement fusion experiments and advancing new ICF platforms.

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Area of Science:

Nuclear Fusion Science
Data Science and Machine Learning
High-Energy Physics

Background:

Recent fusion breakeven at the National Ignition Facility (NIF) highlights the need for advanced data analysis techniques.
Multi-modal data fusion using deep neural networks offers a unified framework for complex experimental datasets.
Challenges in analyzing small experimental datasets necessitate innovative approaches for data augmentation and analysis.

Purpose of the Study:

To summarize neural network methodologies for processing x-ray and neutron imaging data from NIF experiments.
To demonstrate the application of deep learning for automated data analysis, optimization, and uncertainty quantification in fusion research.
To explore the potential of these methods in enhancing the analysis of inertial confinement fusion (ICF) data.

Main Methods:

Implementation of deep neural networks for multi-modal data fusion and automated analysis.
Utilizing model-based physics-informed synthetic data generation to augment small experimental datasets.
Employing generative adversarial networks (GANs) for automated workflows in x-ray and neutron image processing.

Main Results:

Successful application of neural networks for noise emulation, contour analysis, denoising, and super-resolution of imaging data.
Demonstrated effectiveness of physics-informed data generation in compensating for limited experimental data.
Validation of automated workflows for x-ray and neutron image processing in fusion diagnostics.

Conclusions:

Integrated multi-modal imaging analysis using deep neural networks is crucial for optimizing NIF experiments.
These advanced data analysis techniques are vital for the maturation of alternate inertial confinement fusion platforms, including double-shell targets.
Further research in experimental validation and uncertainty quantification will accelerate progress in fusion energy development.