Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910252841041920 |
|---|---|
| author | Fernández-Godino, M. Giselle Shachar, Meir H. Korner, Kevin Belof, Jonathan L. Kumar, Mukul Lind, Jonathan Schill, William J. |
| author_facet | Fernández-Godino, M. Giselle Shachar, Meir H. Korner, Kevin Belof, Jonathan L. Kumar, Mukul Lind, Jonathan Schill, William J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media Fernández-Godino, M. Giselle Shachar, Meir H. Korner, Kevin Belof, Jonathan L. Kumar, Mukul Lind, Jonathan Schill, William J. Machine Learning 68T07 (Primary) 76L05, 65Z05 (Secondary) I.2.6; I.6.3; J.2; I.6.8 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. |
| title | Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media |
| topic | Machine Learning 68T07 (Primary) 76L05, 65Z05 (Secondary) I.2.6; I.6.3; J.2; I.6.8 |
| url | https://arxiv.org/abs/2509.16139 |