Physics-informed machine-learning for modeling aero-optics

J. Nathan Kutz; Diya Sashidhar; Shervin Sahba; Steven L. Brunton; Austin McDaniel; Christopher C. Wilcox

doi:10.1117/12.2596540

11 August 2021 Physics-informed machine-learning for modeling aero-optics

J. Nathan Kutz, Diya Sashidhar, Shervin Sahba, Steven L. Brunton, Austin McDaniel, Christopher C. Wilcox

Proceedings Volume 11817, Applied Optical Metrology IV; 118170E (2021) https://doi.org/10.1117/12.2596540
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

We demonstrate the use of physics-informed machine learning algorithms for the adaptive, real-time characterization of aero-optical systems. From deep learning algorithms to nonlinear control methods, the optical sciences are an ideal platform for integrating data-driven control and machine learning for robust characterization and system identification. For the specific case of aero-optics, the ability to extract dominant coherent structures, transients and turbulent behaviors is critical for a diverse number of applications, including the complex and dynamic aero-optic effects on airborne-based laser platforms. Specifically, aero-optical beam control relies on the development of low-latency predictors that can quickly predict aberrated wavefronts to feed into an adaptive optic control loop. We propose develop a number of data-driven methods, including the dynamic mode decomposition (DMD), for real-time forecasting and control.

Conference Presentation

Citation Download Citation

J. Nathan Kutz, Diya Sashidhar, Shervin Sahba, Steven L. Brunton, Austin McDaniel, and Christopher C. Wilcox "Physics-informed machine-learning for modeling aero-optics", Proc. SPIE 11817, Applied Optical Metrology IV, 118170E (11 August 2021); https://doi.org/10.1117/12.2596540

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