Artificial Intelligence/Machine Learning for Photonics, Power & Energy, Atmospherics, and Quantum Science

Deadline September 30, 2024 2:00 PM EST Investigate ML techniques for real-time atmospheric distortion compensation of laser beam propagation for adaptive optics for directed-energy systems using data from available sensors Investigate and develop algorithms with data optimization and regularization schemes that work with large datasets for deep learning optimal control problems Conduct theoretical analysis and investigation of deep learning algorithms suitable for automatic control of naval power, energy, and thermal management systems Investigate software/hardware implementation of deep learning algorithms Develop data-driven models for certification of boson sampling devices by using unsupervised methods, such as clustering, to find patterns in high-dimensional data that allow algorithms to learn facts about complex quantum systems. This will enable sensitive quantum detectors and sensors.