Fiscal Year	2023
Division	Research & Sponsored Programs
Department	Naval Research Program
Investigator(s)	Cohn, Keith R. Blau, Joseph A.
Sponsor	NPS Naval Research Program (Navy)
Summary	The goal of this study is to develop machine learning (ML) models to predict optical turbulence in the atmospheric surface layer. Turbulence affects the performance of laser weapons and laser communication systems by disrupting the focus of the laser beam. Therefore, it is important to reliably estimate the amount of turbulence along the beam path. Existing physical models often do an excellent job of predicting turbulence in homogenous environments, e.g., over the open ocean. However, it is difficult to model turbulence in more complex environments, such as above the surface of a Navy ship or near the land-sea interface. An alternative approach to physical models is to use machine learning methods such as regression analysis or neural networks. To accomplish the goals of this project, we will gather extensive atmospheric data over many months in the near-coastal region along the Monterey Bay. The collected data will be used to train and validate ML models of optical turbulence. We expect to develop robust models of optical turbulence that will perform well in a variety of environments and conditions. We will also use our ML models to improve the existing physical models by providing insight into the significance of each parameter.
Keywords	artificial intelligence, machine learning, optical turbulence, directed energy, high energy lasers, atmospheric propagation
Publications	Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal
Data	Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal