Causal Multi-fidelity Surrogate Forward and Inverse Models for ICF Implosions
This work addresses the problem of accelerating design and diagnostics in high-energy-density systems like ICF for researchers, though it is incremental as it builds on existing methods for robust features.
The paper tackled the challenge of solving high-dimensional inverse problems in inertial confinement fusion (ICF) by constructing a causal, dynamic, multifidelity surrogate model for the deuterium-tritium interface dynamics, which enabled optimization of radiation temperature drives to reproduce observed dynamics with excellent accuracy.
Continued progress in inertial confinement fusion (ICF) requires solving inverse problems relating experimental observations to simulation input parameters, followed by design optimization. However, such high dimensional dynamic PDE-constrained optimization problems are extremely challenging or even intractable. It has been recently shown that inverse problems can be solved by only considering certain robust features. Here we consider the ICF capsule's deuterium-tritium (DT) interface, and construct a causal, dynamic, multifidelity reduced-order surrogate that maps from a time-dependent radiation temperature drive to the interface's radius and velocity dynamics. The surrogate targets an ODE embedding of DT interface dynamics, and is constructed by learning a controller for a base analytical model using low- and high-fidelity simulation training data with respect to radiation energy group structure. After demonstrating excellent accuracy of the surrogate interface model, we use machine learning (ML) models with surrogate-generated data to solve inverse problems optimizing radiation temperature drive to reproduce observed interface dynamics. For sparse snapshots in time, the ML model further characterizes the most informative times at which to sample dynamics. Altogether we demonstrate how operator learning, causal architectures, and physical inductive bias can be integrated to accelerate discovery, design, and diagnostics in high-energy-density systems.