Surrogate prediction of inertial fusion burn dynamics using improved Fourier neural operator

Abstract

This study focuses on inertial fusion burn via a novel hybrid surrogate model. The proposed hybrid model delivers computational speedup of several orders of magnitude compared with conventional radiation-hydrodynamic simulations, while maintaining high predictive fidelity. Traditional Fourier Neural Operators (FNOs) typically suffer from numerical divergence after 13–16 timesteps; in contrast, the developed FNO-CA model exhibits superior stability and robustness throughout a 50-timestep simulation window. Although FNO and FNO-CA show comparable one-window prediction accuracy, the advantage of FNO-CA becomes evident in long-term rollout, where it better preserves the temporal evolution of key burn quantities. When applied to parameter-space exploration, the model precisely reproduces the highly nonlinear “ignition cliff” phenomenon and reveals the sensitive dependence of fusion gain on initial thermodynamic and boundary conditions. These results demonstrate that the FNO-CA surrogate serves as a powerful, reliable, and efficient tool for the design, optimization, and real-time control of inertial fusion ignition systems.