Modelling burning thermonuclear plasma

Area of Science:

• Plasma Physics
• Fusion Energy
• Astrophysics

Background:

• Significant progress in achieving thermonuclear burn via inertial confinement fusion (ICF) at facilities like the National Ignition Facility (NIF).
• Other drivers, such as the Z-machine, are also advancing towards fusion ignition.
• A burning plasma represents an extreme environment with potential for fundamental physics research.

Purpose of the Study:

• Discuss theoretical challenges in modeling burning plasmas.
• Explore the application of machine learning (ML) for achieving fusion ignition.
• Investigate connections between burning plasmas and fundamental physics, including quantum electrodynamics (QED) and Big Bang conditions.

Main Methods:

• Theoretical modeling of burning plasma regimes.
• Application of novel machine learning algorithms to fusion experiments.
• Analysis of potential fundamental physics discoveries in extreme plasma environments.

Main Results:

• Identified current theoretical modeling gaps for burning plasmas.
• Demonstrated the potential utility of ML in advancing fusion ignition.
• Outlined pathways for exploring QED and early universe physics using fusion plasmas.

Conclusions:

• Achieving a burning plasma is a critical goal for fusion energy.
• Advanced computational methods, including ML, are essential for overcoming modeling challenges.
• Burning plasmas offer a unique platform for groundbreaking discoveries in fundamental physics.