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

2025-09-19

AI Summary

Accurately predicting shock wave propagation in porous and structured materials is critical for planetary defense, national security, and inertial confinement fusion energy, yet conventional methods fail to capture essential physics—including pore collapse, anomalous Hugoniot response, and localized heating. To address this, we propose the Multi-Field Spatio-Temporal Model (MSTM), the first deep learning framework that jointly models seven coupled physical fields (e.g., pressure, density, temperature) with high-fidelity shock front resolution (mass-weighted error <5%). Trained on high-fidelity hydrodynamic simulation data, MSTM integrates an autoregressive architecture with explicit multi-physics spatio-temporal coupling mechanisms. Compared to traditional simulations, it achieves ~1000× inference speedup while maintaining prediction errors below 4% for porous media and 10% for lattice structures. This enables rapid, high-accuracy modeling of dynamic material response—significantly accelerating design and analysis of complex engineered materials.

Technology Category

• Machine Learning: Multimodal Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

The ability to predict how shock waves traverse porous and architected materials is a decisive factor in planetary defense, national security, and the race to achieve inertial fusion energy. Yet capturing pore collapse, anomalous Hugoniot responses, and localized heating -- phenomena that can determine the success of 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 deep learning 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 runs about a thousand times faster than direct simulation, achieving errors below 4% in porous materials and below 10% in lattice structures. Unlike prior single-field or operator-based surrogates, MSTM resolves sharp shock fronts while preserving integrated quantities such as mass-averaged pressure and temperature to within 5%. This advance transforms problems once considered intractable into tractable design studies, establishing a practical framework for optimizing meso-structured materials in planetary impact mitigation, inertial fusion energy, and national security.

Problem

• Predicting shock wave propagation in porous materials
• Modeling coupled multi-field dynamics in meso-structured media
• Capturing pore collapse and localized heating phenomena

Innovation

• Multi-field spatio-temporal deep learning model
• Unifies seven coupled fields autoregressive surrogate
• Runs thousand times faster than simulation