Black Box Geometric Computing with Python: From Theory to Practice

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dc.contributor.authorKoch, Sebastianen_US
dc.contributor.authorSchneider, Teseoen_US
dc.contributor.authorLi, Chengchenen_US
dc.contributor.authorPanozzo, Danieleen_US
dc.contributor.editorFjeld, Morten and Frisvad, Jeppe Revallen_US
dc.date.accessioned2020-05-24T13:06:12Z
dc.date.available2020-05-24T13:06:12Z
dc.date.issued2020
dc.description.abstractThe 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.en_US
dc.description.sectionheadersTutorials
dc.description.seriesinformationEurographics 2020 - Tutorials
dc.identifier.doi10.2312/egt.20201000
dc.identifier.isbn978-3-03868-103-8
dc.identifier.issn1017-4656
dc.identifier.pages1-4
dc.identifier.urihttps://doi.org/10.2312/egt.20201000
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egt20201000
dc.publisherThe Eurographics Associationen_US
dc.subjectMathematics of computing
dc.subjectNumerical analysis
dc.subjectTheory of computation
dc.subjectComputational geometry
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.subjectScientific visualization
dc.subjectMesh models
dc.subjectShape analysis
dc.subjectApplied computing
dc.subjectComputer aided design
dc.titleBlack Box Geometric Computing with Python: From Theory to Practiceen_US
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