Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media
Predicting the extreme hydrodynamic response of porous and architected lattice materials is a fundamental challenge in high energy density physics, where shock-induced pore collapse, baroclinic vorticity, and anomalous kinetic and thermodynamic states must be resolved across multiple scales. Traditional high-fidelity hydrocodes are computationally prohibitive for large-scale design exploration in applications like planetary defense and inertial confinement fusion. We present a multi-field spatio-temporal model (MSTM) designed to overcome the limitations of standard machine learning surrogates, which often fail to capture the sharp gradients and non-linear field couplings characteristic of shock propagation. By training on high-fidelity, multiscale multiphysics data, MSTM simultaneously evolves seven coupled thermodynamic and kinetic fields - including pressure, temperature, density, and velocity - across complex material architectures. Our framework demonstrates the ability to accurately predict anomalous responses, such as counterintuitive post-shock density reductions and localized hotspot formation, with mean root mean squared errors as low as 1.4%. Crucially, the model's multi-field formulation maintains physical consistency and interface stability over long autoregressive rollouts, outperforming single-field models by 94% in structural fidelity. This framework enables a 1000x reduction in time to solution, providing a practical pathway for the real-time analysis and optimization of energy dissipation and momentum transfer in meso-structured media.