Directed Energy Lethality Assessments of Hypersonic Threats

Hypersonic aircraft pose a significant threat to United States defenses due to their over-the-horizon strike capabilities. Low altitude flight at hypersonic speeds makes tracking and engagement using kinetic weapons exceedingly difficult and expensive, necessitating the use of directed energy defense measures providing speed-of-light engagement at a much lower cost. Pulsed high energy laser (HEL)s are a promising solution. The advantages of pulsed lasers over directed energy alternatives include (i) increased directionality and lack of plasms shielding compared to radio frequency (RF) and microwave sources and (ii) near-instant, non-thermal removal of materials resistant to continuous wave (CW) radiation. However, the lethality assessment of pulsed HELs related to hypersonic vehicle vulnerabilities is far from complete. Furthermore, models used to assess pulsed laser lethality mechanisms in hypersonic conditions are extremely slow, burdened by the complex set of timescales and physical processes that must be accounted for. Neural Networks (NNs) trained using Machine Learning (ML) algorithms will allow these complex models to be reduced to simple, few-input programs providing fast and accurate defeat assessments across a broad range of engagement conditions. Approved for Public Release | 24-MDA-11906 (16 Sep 24)