The High Explosives and Affected Targets (HEAT) Dataset
Artificial Intelligence (AI) surrogate models provide a computationally efficient alternative to full-physics simulations, but no public datasets currently exist for training and validating models of high-explosive-driven, multi-material shock dynamics. Simulating shock propagation is challenging due to the need for material-specific equations of state (EOS) and models of plasticity, phase change, damage, fluid instabilities, and multi-material interactions. Explosive-driven shocks further require reactive material models to capture detonation physics. To address this gap, we introduce the High-Explosives and Affected Targets (HEAT) dataset, a physics-rich collection of two-dimensional, c...
| Authors | Bryan Kaiser, Kyle Hickmann, Sharmistha Chakrabarti et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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