The data-driven future of high-energy-density physics

Area of Science:

• High-energy-density physics
• Plasma physics
• Astrophysics
• Nuclear fusion

Background:

• Extreme conditions create highly nonlinear and strongly coupled plasmas.
• Understanding these plasmas is crucial for astrophysics, nuclear fusion, and fundamental physics.
• Traditional theoretical and experimental approaches face challenges due to system complexity.

Purpose of the Study:

• To explore the transformative role of machine learning (ML) and data-driven methods in high-energy-density physics.
• To highlight how ML can overcome the nonlinearities and strong couplings inherent in extreme physical systems.
• To propose a path forward for the research community to leverage these new computational tools.

Main Methods:

• Application of machine learning models to analyze large datasets from high-energy-density experiments.
• Development of data-driven methods for real-time interpretation of diagnostic data.
• Utilizing ML for automatic control of extreme physics facilities and physics model updates.

Main Results:

• ML models can rapidly discover complex interactions within large datasets, improving fundamental understanding.
• Advancements enable real-time data interpretation and automatic control of experiments.
• This shift accelerates the pace of research in extreme physics.

Conclusions:

• Machine learning and data-driven approaches are essential for advancing high-energy-density physics.
• The community needs to adapt research design, training, and best practices to incorporate these methods.
• Investment in synthetic diagnostics and data analysis support is crucial for future progress.