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Anuj (Hrsg.) Karpatne

Knowledge Guided Machine Learning

Accelerating Discovery using Scientific Knowledge and Data

Ebook (EPUB Format)

Machine Learning (ML) methods are increasingly being used as alternatives or surrogates to scientific models to explain real-world phenomena in a number of disciplines. However, given the limited ability of black-box ML methods to learn generalizable and scientifically consistent patterns from limited volumes of data, there is a growing realization in the scientific and data science communities to incorporate scientific knowledge in the ML process. This emerging paradigm combining scientific knowledge and data at an equal footing is labeled Science-Guided ML (SGML). By using scientific consistency as an essential criterion for assessing gener… Mehr

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Produktdetails


Weitere Autoren: Kannan, Ramakrishnan (Hrsg.) / Kumar, Vipin (Hrsg.)
  • ISBN: 978-1-00-059813-1
  • EAN: 9781000598131
  • Produktnummer: 38184656
  • Verlag: Taylor & Francis Ltd.
  • Sprache: Englisch
  • Erscheinungsjahr: 2022
  • Seitenangabe: 472 S.
  • Plattform: EPUB
  • Auflage: 1. Auflage
  • Abbildungen: 8 schwarz-weiße und 170 farbige Abbildungen, 1 farbige Fotos, 7 schwarz-weiße und 170 farbige Zeichnungen, 38 schwarz-weiße Tabellen

Über den Autor


Anuj Karpatne is an Assistant Professor in the Department of Computer Science at Virginia Tech. His research focuses on pushing on the frontiers of knowledge-guided machine learning by combining scientific knowledge and data in the design and learning of machine learning methods to solve scientific and societally relevant problems.Ramakrishnan Kannan is the group leader for Discrete Algorithms at Oak Ridge National Laboratory. His research expertise is in distributed machine learning and graph algorithms on HPC platforms and their application to scientific data with a specific interest for accelerating scientific discovery.Vipin Kumar is a Regents Professor at the University of Minnesota's Computer Science and Engineering Department. His current major research focus is on knowledge-guided machine learning and its applications to understanding the impact of human induced changes on the Earth and its environment.

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