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

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.