[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119851-en":3,"doc-seo-119851-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119851,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning for Aero-Optical Wavefront Characterization and Forecasting - dissertation","Laser-based free-space communication faces major obstacles from atmospheric turbulence and fluid-flow-induced refractive-index fluctuations that generate rapidly varying aberrated wavefronts. Aero-optics addresses these effects, enabling adaptive-optics correction to improve coherent transmission, including applications from energy links to secure quantum communications. This dissertation develops low-latency, data-driven predictive methods using machine learning. It presents three techniques—optimized Dynamic Mode Decomposition, shallow-decoder sensor fusion, and recurrent-network wavefront forecasting—to enable robust aero-optical wavefront sensing and phase-distortion forecasting.","Machine Learning for Aero-Optical Wavefront Characterization and  \nForecasting  \nShervin Sahba  \nA dissertation  \nsubmitted in partial fulfillment of the  \nrequirements for the degree of  \nDoctor of Philosophy  \nUniversity of Washington  \n2023  \nReading Committee:  \nJ. Nathan Kutz, Chair  \nArka Majumdar  \nShih-Chieh Hsu  \nProgram Authorized to Offer Degree:  \nPhysics  \n©Copyright 2023 Shervin Sahba  \nUniversity of Washington  \nAbstract  \nMachine Learning for Aero-Optical Wavefront Characterization and Forecasting  \nShervin Sahba  \nChair of the Supervisory Committee:  \nJ. Nathan Kutz  \nDepartment of Applied Mathematics  \nThe laser is a masterwork of the previous century of physics, but to harness its power coherently in the turbulent wilds of the sky remains a challenge. Free-space lasing hosts myriad applications, from direct energy transmissions for defense to secure communication channels that robustly support quantum entanglement. Aero-optics is the multi-disciplinary field underpinning such optical transmissions through atmosphere and fluid flows. Turbulent variations in the index of refraction, like those forming around boundary layers of airborne optical platforms, manifest aberrated wavefronts. Forecasting these rapid phase distortions allows us to rectify laser transmissions via adaptive optic engineering. Doing so hinges on the development of low-latency predictive techniques, for which we turn to advancements in data-driven algorithms and deep learning.  \nThis thesis thus introduces key concepts of aero-optics, wavefront sensing, and machine learning for data-driven physics. We then demonstrate three machine learning methodologies-optimized Dynamic Mode Decomposition, sensor fusion through shallow decoder networks, and forecasting via recurrent neural networks with shallow decoder outputs — for robust aero-optical wavefront sensing.  \nTABLE OF CONTENTS  \nPage  \nList of Figures ....................................... iii  \nList of Tables ........................................ v  \n[Glossary ........................................... vi](Glossary ........................................... vi)  \n[Chapter 1: Aero-Optics ................................](Chapter 1: Aero-Optics ................................). 1  \n1.1 Aero-Optical Effects and Turbulent Boundary Layers ............. 3  \n1.2 Adaptive Optics .................................. 4  \nChapter 2: Sensors ................................... 7  \n2.1 Shack-Hartmann Wavefront Sensor ....................... 9  \n2.2 Digital Holography Wavefront Sensor ...................... 11  \n2.3 Sensor Fusion ................................... 12  \nChapter 3: Machine Learning ............................. 14  \n3.1 Dynamic Mode Decomposition .......................... 15  \n3.2 Optimized Dynamic Mode Decomposition ................... 17  \n3.3 Feed-forward Neural Networks .......................... 18  \n3.4 Recurrent Neural Networks ............................ 20  \nChapter 4: Dynamic mode decomposition for aero-optic wavefront characterization 23  \n4.1 Summary ..................................... 23  \n4.2 Introduction .................................... 24  \n4.3 AAOL-T Experimental Data ........................... 27  \n4.4 Sensors and Data Acquisition .......................... 28  \n4.5 Optimized Dynamic Mode Decomposition ................... 29  \n4.6 Results and Analysis ............................... 32  \n4.7 Conclusion ..................................... 34  \nChapter 5: Sensor Fusion for Aero-Optics ....................... 43  \n5.1 Summary ..................................... 43  \n5.2 Introduction .................................... 43  \n5.3 Aero-Optics .................................... 46  \n5.4 Sensor Fusion: Resolution fusion ......................... 51  \n5.5 Sensor Fusion: Multi-modal sensors ....................... 54  \n5.6 Conclusions .................................... 55  \nChapter 6: Shallow Recurrent Network Wavefront Forecasting ........... 62  \n6.1 Summar","cbCaiiePv8xe7sKk","https://ap.wps.com/l/cbCaiiePv8xe7sKk","pdf",20208935,1,100,"English","en",105,"# Abstract\n# Chapter 1: Aero-Optics\n## Aero-Optical Effects and Turbulent Boundary Layers\n## Adaptive Optics\n# Chapter 2: Sensors\n## Shack-Hartmann Wavefront Sensor\n## Digital Holography Wavefront Sensor\n## Sensor Fusion\n# Chapter 3: Machine Learning\n## Dynamic Mode Decomposition\n## Optimized Dynamic Mode Decomposition\n## Feed-forward Neural Networks\n## Recurrent Neural Networks\n# Chapter 4: Dynamic mode decomposition for aero-optic wavefront characterization\n## Summary and Introduction\n## Experimental Data and Data Acquisition\n## Results and Analysis\n## Conclusion\n# Chapter 5: Sensor Fusion for Aero-Optics\n## Summary and Introduction\n## Sensor Fusion: Resolution fusion\n## Sensor Fusion: Multi-modal sensors\n## Conclusions\n# Chapter 6: Shallow Recurrent Network Wavefront Forecasting\n## Summary and Introduction\n## Methods and Data\n## Results and Conclusion\n# Chapter 7: Conclusion\n# Bibliography","[{\"question\":\"Which machine learning methods are introduced for robust wavefront sensing and forecasting?\",\"answer\":\"The dissertation demonstrates three methodologies: optimized Dynamic Mode Decomposition (including feed-forward optimization), sensor fusion using shallow decoder networks, and forecasting with recurrent neural networks with shallow-decoder outputs.\"}]","Machine Learning for Aero-Optical Wavefront Characterization and Forecasting - 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