Physics Constrained Machine Learning Professors in Global

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Tal Kachman
Radboud University Netherlands
machine learning
statistical physics
reinforcement learning
deep learning
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Pierre Flener
Uppsala University Sweden
constraint modeling
constraint programming
constraint solving
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Vinay Gogineni
University of Southern Denmark Denmark
physics-informed neural networks
quantum machine learning
machine learning
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Danica Sutherland
University of British Columbia Canada
theoretical machine learning
statistical machine learning
machine learning
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Satish Thittamaranahalli
University of Southern California United States of America
computational physics
machine learning
constraint reasoning
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Satish Thittamaranahalli
University of Southern California United States of America
computational physics
machine learning
constraint reasoning
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Satish Thittamaranahalli
University of Southern California United States of America
computational physics
machine learning
constraint reasoning
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Niels Walet
University of Manchester United Kingdom
computational physics
condensed matter physics
theoretical physics
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Satyasaran Changdar
University of Copenhagen Denmark
physics-informed neural networks
machine learning
deep learning
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Hina Shaheen
University of Manitoba Canada
statistical machine learning
machine learning
computational modelling
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Nathaniel Bastian
Pennsylvania State University United States of America
federated machine learning
machine learning
deep learning
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Somnath Ghosh
Johns Hopkins University United States of America
multi-physics modeling
machine learning
computational mechanics
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Michiel Van De Panne
University of British Columbia Canada
physics-based simulation
machine learning
reinforcement learning
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Martin Kröger
ETH Zurich Switzerland
computational physics
theoretical physics
statistical physics
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Rudolf Römer
University of Warwick United Kingdom
computational physics
mathematical physics
condensed matter theory
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Samy Bengio
École Polytechnique Fédérale de Lausanne Switzerland
statistical machine learning
machine learning
deep learning
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Peter Mcmahon
Cornell University Netherlands
condensed matter physics
machine learning
material physics
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Jessica Sorrell
Johns Hopkins University United States of America
machine learning
machine learning algorithms
machine learning theory
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Margherita Disertori
University of Bonn Germany
condensed matter physics
mathematical physics
boundary physics
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Chad Williams
University of Victoria Canada
machine learning
computational modelling
reinforcement learning
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David Brückner
University of Basel Switzerland
machine learning
theoretical physics
statistical physics
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