Causal Multi-fidelity Surrogate Forward and Inverse Models for ICF Implosions
Researchers have developed a novel approach using machine learning to solve complex inverse problems in inertial confinement fusion (ICF) implosions. This method integrates operator learning, causal architectures, and physical inductive biases to create a multi-fidelity surrogate model. This model maps radiation temperature drives to the dynamics of the deuterium-tritium (DT) interface, enabling more accurate predictions and optimization for ICF experiments. AI
IMPACT This approach could significantly speed up the discovery and design process for fusion energy systems.