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Physics-informed machine learning meets engineering seminar series

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Paris Perdikaris - PirateNets: Physics informed Deep Learning with Residual Adaptive Networks

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Tobias Heinrich Nagel - Kalman Bucy informed Neural Networks for System Identification

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Sascha Ranftl - A Connection between Probability, Physics and Neural Network

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Thomas Markovich - Quantum Diffusion Convolution Kernels on Graphs

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Ameya Jagtap Enhancing Scientific Computing Through Physics informed Neural Networks

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Karan Taneja - Feature Encoded and Multi-Resolution Physics-Informed Machine Learning Approaches...

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Hongbo Zhao - Learning Battery Physics from Images

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Jan Olle - Discovering Quantum Error Correcting Codes with Reinforcement Learning

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Nathan Kutz - The Dynamic Mode Decomposition - A Data-Driven Algorithm

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Cristian Axenie - Physics-informed Machine Learning for Robust Pedestrian Detection

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Chris Rackauckas - Generalizing Scientific Machine Learning and Differentiable Simulation

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Morten Mattrup Smedskjær - Cracking the Code

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Arnaud Vadeboncoeur - On Random Grid Neural Processes for Solving Forward and Inverse Problems

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Maldon Goodridge - A rare event study of frequency regulation and contingency services...

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Jonathan Smith - HypoSVI: Earthquake hypocentre inversion with Stein variational inference...

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