physics informed

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1. 1▶Paris Perdikaris - PirateNets: Physics informed Deep Learning with Residual Adaptive NetworksPhysics-informed machine learning meets engineering seminar seriesNotes
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2. 2▶Tobias Heinrich Nagel - Kalman Bucy informed Neural Networks for System IdentificationPhysics-informed machine learning meets engineering seminar seriesNotes
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3. 3▶Sascha Ranftl - A Connection between Probability, Physics and Neural NetworkPhysics-informed machine learning meets engineering seminar seriesNotes
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4. 4▶Thomas Markovich - Quantum Diffusion Convolution Kernels on GraphsPhysics-informed machine learning meets engineering seminar seriesNotes
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5. 5▶AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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6. 6▶AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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7. 7▶AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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8. 8▶AI/ML+Physics Part 4: Crafting a Loss Function [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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9. 9▶AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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10. 10▶AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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11. 11▶Ameya Jagtap Enhancing Scientific Computing Through Physics informed Neural NetworksPhysics-informed machine learning meets engineering seminar seriesNotes
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12. 12▶Karan Taneja - Feature Encoded and Multi-Resolution Physics-Informed Machine Learning Approaches...Physics-informed machine learning meets engineering seminar seriesNotes
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13. 13▶AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]Physics Informed Machine LearningNotes
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14. 14▶Physics Informed Machine Learning: High Level Overview of AI and ML in Science and EngineeringPhysics Informed Machine LearningNotes
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15. 15▶Hongbo Zhao - Learning Battery Physics from ImagesPhysics-informed machine learning meets engineering seminar seriesNotes
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16. 16▶Jan Olle - Discovering Quantum Error Correcting Codes with Reinforcement LearningPhysics-informed machine learning meets engineering seminar seriesNotes
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17. 17▶Nathan Kutz - The Dynamic Mode Decomposition - A Data-Driven AlgorithmPhysics-informed machine learning meets engineering seminar seriesNotes
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18. 18▶Cristian Axenie - Physics-informed Machine Learning for Robust Pedestrian DetectionPhysics-informed machine learning meets engineering seminar seriesNotes
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19. 19▶Chris Rackauckas - Generalizing Scientific Machine Learning and Differentiable SimulationPhysics-informed machine learning meets engineering seminar seriesNotes
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20. 20▶Morten Mattrup Smedskjær - Cracking the CodePhysics-informed machine learning meets engineering seminar seriesNotes
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21. 21▶Arnaud Vadeboncoeur - On Random Grid Neural Processes for Solving Forward and Inverse ProblemsPhysics-informed machine learning meets engineering seminar seriesNotes
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22. 22▶Maldon Goodridge - A rare event study of frequency regulation and contingency services...Physics-informed machine learning meets engineering seminar seriesNotes
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23. 23▶Jonathan Smith - HypoSVI: Earthquake hypocentre inversion with Stein variational inference...Physics-informed machine learning meets engineering seminar seriesNotes
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24. 24▶Discrepancy Modeling with Physics Informed Machine LearningPhysics Informed Machine LearningNotes
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25. 25▶Sparse Nonlinear Dynamics Models with SINDy, Part 5: The Optimization AlgorithmsPhysics Informed Machine LearningNotes
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26. 26▶Sparse Nonlinear Dynamics Models with SINDy, Part 4: The Library of Candidate NonlinearitiesPhysics Informed Machine LearningNotes
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27. 27▶Sparse Nonlinear Dynamics Models with SINDy, Part 3: Effective Coordinates for Parsimonious ModelsPhysics Informed Machine LearningNotes
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28. 28▶Sparse Nonlinear Dynamics Models with SINDy, Part 2: Training Data & Disambiguating ModelsPhysics Informed Machine LearningNotes
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29. 29▶Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!Physics Informed Machine LearningNotes
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30. 30▶Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine LearningPhysics Informed Machine LearningNotes
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