Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap
  with Extremely Limited Data
Matthew L. Olson Shusen Liu Jayaraman J. Thiagarajan Bogdan Kustowski Weng-Keen Wong Rushil Anirudh
Recent advances in machine learning, specifically transformer architecture,
have led to significant advancements in commercial domains. These powerful
models have demonstrated superior capability to learn complex relationships and
often generalize better to new data and problems. This paper presents a novel
transformer-powered approach for enhancing prediction accuracy in multi-modal
output scenarios, where sparse experimental data is supplemented with
simulation data. The proposed approach integrates transformer-based
architecture with a novel graph-based hyper-parameter optimization technique.
The resulting system not only effectively reduces simulation bias, but also
achieves superior prediction accuracy compared to the prior method. We
demonstrate the efficacy of our approach on inertial confinement fusion
experiments, where only 10 shots of real-world data are available, as well as
synthetic versions of these experiments.