Physics-based Modeling Meets Machine Learning

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The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in scientific and visual computing. This talk covers a selection of previous and current work presenting various synergy effects between physics-based modeling and machine learning ranging from the generation and the enhancement of synthetic data to data-driven approaches boosting the performance of physics simulations.

Brief Biography

Since summer 2016, Prof. Michels is heading the Computational Sciences Group within KAUST's Visual Computer Center. Previously, he joined Stanford University in fall 2014 heading the High Fidelity Algorithmics Group within the Max Planck Center for Visual Computing and Communication. Prior to this, he did postdoctoral studies in Computing and Mathematical Sciences at Caltech in spring and summer 2014. He studied Computer Science and Physics at the University of Bonn from where he received a B.Sc. in Computer Science and Physics in 2011, a M.Sc. in Computer Science in 2013, and a Ph.D. from the Faculty of Mathematics and Natural Sciences (Dr.rer.nat.) in early 2014.

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