Directed Energy Lethality Assessments of Hypersonic Threats

The modern defense industry faces a critical challenge in countering sophisticated, maneuvering hypersonic velocity threats, demanding innovative strategies and technologies. Our proposal introduces a groundbreaking approach using machine learning (ML) techniques, specifically Physics-Informed Machine Learning (PIML) and Lagrangian Neural Networks (LNNs), to revolutionize survivability and lethality (S&L) modeling, simulation, and analysis. Our methodology addresses the limitations of current low- and high-fidelity modeling tools, which either lack robustness and adaptability or require immense computational resources. LNNs, a class of PIML, merge classical mechanics with ML to construct highly accurate digital twins of target components and systems. This approach promises enhanced generalization, extrapolation potential, and modularity, thereby overcoming the challenges of speed and accuracy faced by existing models. In Phase I, we propose to develop a detailed plan for this innovative technology, complemented by the construction of a small-scale prototype. This phase will focus on exploring and validating the proposed method's limitations and capabilities. Our approach aims to balance fidelity with computational efficiency, ensuring robustness against varying scenarios and parameters, while remaining capable of capturing important multiphysics phenomena pertinent to hypersonic engagements. The broader vision encompasses a complete Data-Driven Survivability and Lethality Framework, integrating prediction and analysis components for comprehensive S&L assessments. This framework will not only benefit military applications, such as weapon concept evaluation, design requirement development, and wargaming but with core functionality that also has significant potential applications in commercial sectors for predictive maintenance and system health monitoring. Our proposal outlines a pathway to a versatile, reliable, efficient, and intelligible solution in S&L modeling. We anticipate delivering a tool that achieves a balance among these key capabilities, significantly enhancing data-driven decision-making in defense and beyond. Approved for Public Release | 24-MDA-11906 (16 Sep 24)