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    Black Box Geometric Computing with Python: From Theory to Practice

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    Date
    2020
    Author
    Koch, Sebastian
    Schneider, Teseo
    Li, Chengchen
    Panozzo, Daniele
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    Abstract
    The first part of the course is theoretical, and introduces the finite element method trough interactive Jupyter notebooks. It also covers recent advancements toward an integrated pipeline, considering meshing and element design as a single challenge, leading to a black box pipeline that can solve simulations on ten thousand in the wild meshes, without any parameter tuning. In the second part we will move to practice, introducing a set of easy-to-use Python packages for applications in geometric computing. The presentation will have the form of live coding in a Jupyter notebook. We have designed the presented libraries to have a shallow learning curve, while also enabling programmers to easily accomplish a wide variety of complex tasks. Furthermore, these libraries utilize NumPy arrays as a common interface, making them highly composable with each-other as well as existing scientific computing packages. Finally, our libraries are blazing fast, doing most of the heavy computations in C++ with a minimal constant-overhead interface to Python. In the course, we will present a set of real-world examples from geometry processing, physical simulation, and geometric deep learning. Each example is prototypical of a common task in research or industry and is implemented in a few lines of code. By the end of the course, attendees will have exposure to a swiss-army-knife of simple, composable, and high-performance tools for geometric computing.
    BibTeX
    @inproceedings {t.20201000,
    booktitle = {Eurographics 2020 - Tutorials},
    editor = {Fjeld, Morten and Frisvad, Jeppe Revall},
    title = {{Black Box Geometric Computing with Python: From Theory to Practice}},
    author = {Koch, Sebastian and Schneider, Teseo and Li, Chengchen and Panozzo, Daniele},
    year = {2020},
    publisher = {The Eurographics Association},
    ISSN = {1017-4656},
    ISBN = {978-3-03868-103-8},
    DOI = {10.2312/egt.20201000}
    }
    URI
    https://doi.org/10.2312/egt.20201000
    https://diglib.eg.org:443/handle/10.2312/egt20201000
    Collections
    • EG 2020 - Tutorials

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    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
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