A Deep Learning Approach to Design Inertial Confinement Fusion Implosions

The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. The deep learning model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.”¹ The resulting deep-learning model can reproduce key experimental observables with high accuracy and inference times on the resulting model are unprecedented relative to those achieved with simulation codes. We use the model to search for a design that maximizes the experimental ignition threshold factor by iterating through input parameter space. Once a high-performing design is identified, high-fidelity simulations are used to understand the key physics of the design. This material is based upon work supported by the Department of Energy National Nuclear Security Administration under Award Number DE-NA0003856.

1 K. D. Humbird et al., IEEE Trans. Plasma Sci. 48, 61 (2020).