The High Explosives and Affected Targets (HEAT) Dataset

2026-04-20

AI Summary

This work addresses the current lack of publicly available datasets for training and validating AI models in high-explosive-driven multi-material shock dynamics. The authors present the HEAT dataset, generated using Los Alamos National Laboratory’s Eulerian multi-material shock propagation code, which provides high-fidelity two-dimensional simulations encompassing both cylindrical (CYL) and planar (PLI) configurations. The dataset integrates equations of state, reactive material models, and multi-physics coupling, capturing critical phenomena such as shock propagation, plastic deformation, phase transitions, and interfacial instabilities. Notably, HEAT is the first public dataset to include full thermo-mechanical-kinematic time-resolved fields for diverse material combinations involving explosives, metals, polymers, and gases, thereby establishing a reliable benchmark for developing and validating AI/ML models in complex shock physics.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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, cylindrically symmetric simulations generated using an Eulerian multi-material shock-propagation code developed at Los Alamos National Laboratory. HEAT consists of two partitions: expanding shock-cylinder (CYL) simulations and Perturbed Layered Interface (PLI) simulations. Each entry includes time series of thermodynamic fields (pressure, density, temperature), kinematic fields (position, velocity), and continuum quantities such as stress. The CYL partition spans a range of materials, including metals (aluminum, copper, depleted uranium, stainless steel, tantalum), a polymer, water, gases (air, nitrogen), and a detonating material. The PLI partition explores varied geometries with fixed materials: copper, aluminum, stainless steel, polymer, and high explosive. HEAT captures key phenomena such as shock propagation, momentum transfer, plastic deformation, and thermal effects, providing a benchmark dataset for AI/ML models of multi-material shock physics.

Problem

• high-explosive-driven shock dynamics
• multi-material interactions
• surrogate models
• dataset
• shock propagation

Innovation

• surrogate modeling
• multi-material shock dynamics
• high-explosive dataset
• physics-informed AI
• equation of state