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Full-Text Articles in Physics
Polarization-Selective Modulation Of Supercavity Resonances Originating From Bound States In The Continuum, Chan Kyaw, Riad Yahiaoui, Joshua A. Burrow, Viet Tran, Kyron Keelen, Wesley Sims, Eddie C. Red, Willie S. Rockward, Mikkel A. Thomas, Andrew M. Sarangan, Imad Agha, Thomas A. Searles
Polarization-Selective Modulation Of Supercavity Resonances Originating From Bound States In The Continuum, Chan Kyaw, Riad Yahiaoui, Joshua A. Burrow, Viet Tran, Kyron Keelen, Wesley Sims, Eddie C. Red, Willie S. Rockward, Mikkel A. Thomas, Andrew M. Sarangan, Imad Agha, Thomas A. Searles
Electro-Optics and Photonics Faculty Publications
Bound states in the continuum (BICs) are widely studied for their ability to confine light, produce sharp resonances for sensing applications and serve as avenues for lasing action with topological characteristics. Primarily, the formation of BICs in periodic photonic band gap structures are driven by symmetry incompatibility; structural manipulation or variation of incidence angle from incoming light. In this work, we report two modalities for driving the formation of BICs in terahertz metasurfaces. At normal incidence, we experimentally confirm polarization driven symmetry-protected BICs by the variation of the linear polarization state of light. In addition, we demonstrate through strong coupling …
Atmospheric Turbulence Study With Deep Machine Learning Of Intensity Scintillation Patternsartem, Artem V. Vorontsov, Mikhail A. Vorontsov, Grigorii A. Fillimonov, Ernst Polnau
Atmospheric Turbulence Study With Deep Machine Learning Of Intensity Scintillation Patternsartem, Artem V. Vorontsov, Mikhail A. Vorontsov, Grigorii A. Fillimonov, Ernst Polnau
Electro-Optics and Photonics Faculty Publications
A new paradigm for machine learning-inspired atmospheric turbulence sensing is developed and applied to predict the atmospheric turbulence refractive index structure parameter using deep neural network (DNN)-based processing of short-exposure laser beam intensity scintillation patterns obtained with both: experimental measurement trials conducted over a 7 km propagation path, and imitation of these trials using wave-optics numerical simulations. The developed DNN model was optimized and evaluated in a set of machine learning experiments. The results obtained demonstrate both good accuracy and high temporal resolution in sensing. The machine learning approach was also employed to challenge the validity of several eminent atmospheric …
Artificial Neural Network Discovery Of A Switchable Metasurface Reflector, J. R. Thompson, J. A. Burrow, P. J. Shah, J. Slagle, E. S. Harper, A. Van Rynbach, I. Agha, M. S. Mills
Artificial Neural Network Discovery Of A Switchable Metasurface Reflector, J. R. Thompson, J. A. Burrow, P. J. Shah, J. Slagle, E. S. Harper, A. Van Rynbach, I. Agha, M. S. Mills
Electro-Optics and Photonics Faculty Publications
Optical materials engineered to dynamically and selectively manipulate electromag- netic waves are essential to the future of modern optical systems. In this paper, we simulate various metasurface configurations consisting of periodic 1D bars or 2D pillars made of the ternary phase change material Ge2Sb2Te5 (GST). Dynamic switching behavior in reflectance is exploited due to a drastic refractive index change between the crystalline and amorphous states of GST. Selectivity in the reflection and transmission spectra is manipulated by tailoring the geometrical parameters of the metasurface. Due to the immense number of possible metasurface configurations, we train deep neural networks capable of …