A Machine Learning-Accelerated Framework for Lethality Assessment of Directed Energy Engagements of Hypersonic Threats

ATA Engineering, Inc., (ATA) proposes a Phase II Small Business Innovation Research program to develop a modular framework for rapid assessment of the lethality of directed energy (DE) engagements against hypersonic threat systems. The developed framework will couple existing tools in the lethality and DE communities with state-of-the-art, physics-based solvers for modeling the threat’s aerothermal condition, laser-material interaction effects, and associated aerodynamic performance degradation. Physics-based methods will be accelerated using a variety of machine learning approaches to generate reduced-order models and enable rapid lethality assessments by software end users. The Phase II effort will focus on executing the development and verification and validation (V&V) plans produced in Phase I. The development and V&V effort will utilize the physics-based tools identified in Phase I for generation and training of machine learning reduced-order models, produce a graphical user interface to enable user interaction with the lethality framework, and demonstrate the lethality framework on an engagement scenario of interest. Approved for Public Release | 25-MDA-12220 (23 Sep 25)