Sensor limitations often result in devices with particularly high spatial-imaging resolution or high sampling rates but not both concurrently. Adaptive optics control mechanisms, for example, rely on high-fidelity sensing technology to predictively correct wavefront phase aberrations. We propose fusing these two categories of sensors: those with high spatial resolution and those with high temporal resolution. As a prototype, we first sub-sample simulations of the Kuramoto-Sivashinsky equation, known for its chaotic flow from diffusive instability, and build a map between such simulated sensors using a Shallow Decoder Neural Network. We then examine how to fuse the merits of a common sensor in aero-optical sensing, the Shack-Hartmann wavefront sensor, with the increased spatial information of a Digital Holography wavefront sensor, training on supersonic wind-tunnel wavefront data provided by the Aero-Effects Laboratory at the Air Force Research Laboratory Directed Energy Directorate. These maps merge the high-temporal and high-spatial resolutions from each respective sensor, demonstrating a proof-of-concept for wavefront sensor fusion for adaptive optical applications.
Aero-optical beam control relies on the development of low-latency forecasting techniques to quickly predict wavefronts aberrated by the turbulent boundary layer around an airborne optical system, and its study applies to a multidomain need from astronomy to microscopy for high-fidelity laser propagation. We leverage the forecasting capabilities of the dynamic mode decomposition (DMD) — an equation-free, data-driven method for identifying coherent flow structures and their associated spatiotemporal dynamics — to estimate future state wavefront phase aberrations to feed into an adaptive optic control loop. We specifically leverage the optimized DMD (opt-DMD) algorithm on a subset of the Airborne Aero-Optics Laboratory-Transonic experimental dataset, characterizing aberrated wavefront dynamics for 23 beam propagation directions via the spatiotemporal decomposition underlying DMD. Critically, we show that opt-DMD produces an optimally debiased eigenvalue spectrum with imaginary eigenvalues, allowing for arbitrarily long forecasting to produce a robust future state prediction, while exact DMD loses structural information due to modal decay rates.
KEYWORDS: Digital micromirror devices, Control systems, Data modeling, Wavefronts, Systems modeling, Machine learning, Transient nonlinear optics, Near field optics, Mathematical modeling, Algorithm development
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.
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