Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media

Abstract

The ability to predict how shock waves traverse porous and architected materials is a key challenge in planetary defense and in the pursuit of inertial fusion energy. Yet capturing pore collapse, anomalous Hugoniot responses, and localized heating - phenomena that strongly influence asteroid deflection or fusion ignition - has remained a major challenge despite recent advances in single-field and reduced representations. We introduce a multi-field spatio-temporal model (MSTM) that unifies seven coupled fields - pressure, density, temperature, energy, material distribution, and two velocity components - into a single autoregressive surrogate. Trained on high-fidelity hydrocode data, MSTM captures nonlinear shock-driven dynamics across porous and architected configurations, achieving mean errors of 1.4% and 3.2% respectively, all while delivering over three orders of magnitude in speedup. MSTM reduces mean-squared error and structural dissimilarity by 94% relative torelative to single-field spatio-temporal models. This advance transforms problems once considered intractable into tractable design studies, establishing a practical framework for optimizing meso-structured materials in planetary impact mitigation and inertial fusion energy.