arXiv:2509.16139v5 Announce Type: replace Abstract: 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
The increasing computational demands of simulating extreme hydrodynamic events, coupled with advances in deep learning, are enabling new approaches to complex physics problems.
This development allows for faster and more efficient simulation of critical high-energy density physics applications, previously limited by computational costs, impacting national security and energy research.
The ability to rapidly model complex material responses under extreme conditions changes the approach to designing materials and systems for applications like planetary defense and inertial confinement fusion.
- · Defense contractors
- · National security sector
- · Energy research institutions
- · AI/ML research labs
- · Traditional high-fidelity hydrocode developers (without AI integration)
Computational bottlenecks in simulating extreme hydrodynamic responses are significantly reduced.
Accelerated design and testing cycles for advanced materials and high-energy systems, potentially leading to breakthroughs in fusion energy or defense applications.
New classes of materials with unprecedented resilience and performance under extreme conditions become viable through AI-driven design and simulation.
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Read at arXiv cs.LG