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Table of contents (20 papers)
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Front Matter
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Data Reduction Techniques
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Front Matter
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New Research Results and Looking Forward
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Front Matter
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About this book
Specifically, this book contains chapters that reflect state-of-the-art research results and best practices in the area of in situ visualization. Our target audience are researchers and practitioners from the areas of mathematics computational science, high-performance computing, and computer science that work on or with in situ techniques, or desire to do so in future.
Editors and Affiliations
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Computer and Information Science, University of Oregon, Eugene, USA
Hank Childs
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Extreme-Scale Data Science and Analytics, Sandia National Laboratories, Livermore, USA
Janine C. Bennett
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Scientific Visualization Lab, University of Kaiserslautern, Kaiserslautern, Germany
Christoph Garth
Bibliographic Information
Book Title: In Situ Visualization for Computational Science
Editors: Hank Childs, Janine C. Bennett, Christoph Garth
Series Title: Mathematics and Visualization
DOI: https://doi.org/10.1007/978-3-030-81627-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-81626-1Published: 05 May 2022
Softcover ISBN: 978-3-030-81629-2Published: 06 May 2023
eBook ISBN: 978-3-030-81627-8Published: 04 May 2022
Series ISSN: 1612-3786
Series E-ISSN: 2197-666X
Edition Number: 1
Number of Pages: XV, 460
Number of Illustrations: 17 b/w illustrations, 184 illustrations in colour
Topics: Visualization, Computational Science and Engineering