Knowledge Guided Machine Learning : Accelerating Discovery Using Scientific Knowledge and Data

Karpatne, Anuj (EDT); Kannan, Ramakrishnan (EDT); Kumar, Vipin (EDT)

ISBN 10: 0367693410 / ISBN 13: 9780367693411

Edité par Chapman and Hall/CRC, 2022

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Détails bibliographiques

• Titre: Knowledge Guided Machine Learning : Accelerating Discovery Using Scientific Knowledge and Data
• Éditeur: Chapman and Hall/CRC
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 0367693410
• ISBN à 13 chiffres: 9780367693411
• Reliure: Couverture rigide
• État de l'article: As New

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Synopsis

Knowledge Guided Machine Learning provides an introduction to this rapidly growing field by discussing some of the common themes of research in SGML, using illustrative examples and case studies from diverse application domains and research communities as contributed book chapters.

À propos de l?auteur

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.