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stresses. While effective, this mechanism relies on energy-consuming and irreversible adhesion. A new and fascinating paradigm has recently emerged: geometric cohesion, the spontaneous emergence
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stresses. While effective, this mechanism relies on energy-consuming and irreversible adhesion. A new and fascinating paradigm has recently emerged: geometric cohesion, the spontaneous emergence
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account of the history of geometric mechanics is given in these slides: https://klasmodin.github.io/assets/pdf/modin-geometric-mechanics-lund-2023.pdf More posts related to our research are available here
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algebra, topology, geometry and convexity Ohad Noy Feldheim Probability, Gaussian processes, discrete probability, extremal combinatorics Zlil Sela Geometric group theory, model theory Alexander Sodin
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, see the Geometric Machine Learning Group's website: https://weber.seas.harvard.edu For questions, please email mweber@seas.harvard.edu . Applications will be reviewed on a rolling basis. Basic
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Model. Int. J. Hydrog. Energy 2014, 39 (9), 4516–4530. https://doi.org/10.1016/j.ijhydene.2014.01.036. ; [3] Carral, C.; Mele, P. Modeling the Original and Cyclic Compression Behavior of Non-Woven
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flows, or the motion of charged fluids such as plasmas. A brief account of the history of geometric mechanics is given in these slides: https://klasmodin.github.io/assets/pdf/modin-geometric-mechanics
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demanding real-world geometries and to build specialist expertise in high order mesh generation, geometric modelling and CAD to CAE integration. These capabilities are of growing importance to industry yet
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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consumption and turnaround time, placing increasing pressure on sustainability targets. Understanding how geometric changes influence the flow, thermal or structural response remains a major challenge, and