Knowledge Guided Machine Learning (eBook, ePUB)

Accelerating Discovery using Scientific Knowledge and Data
Redaktion: Karpatne, Anuj; Kumar, Vipin; Kannan, Ramakrishnan

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 442
• Erscheinungstermin: 15. August 2022
• Englisch
• ISBN-13: 9781000598131
• Artikelnr.: 64215748

Autorenporträt

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.

Inhaltsangabe

About the Editors. List of Contributors. 1 Introduction. 2 Targeted Use of
Deep Learning for Physics and Engineering. 3 Combining Theory and
Data-Driven Approaches for Epidemic Forecasts. 4 Machine Learning and
Projection-Based Model Reduction in Hydrology and Geosciences. 5
Applications of Physics-Informed Scientific Machine Learning in Subsurface
Science: A Survey. 6 Adaptive Training Strategies for Physics-Informed
Neural Networks. 7 Modern Deep Learning for Modeling Physical Systems. 8
Physics-Guided Deep Learning for Spatiotemporal Forecasting. 9
Science-Guided Design and Evaluation of Machine Learning Models: A
Case-Study on Multi-Phase Flows. 10 Using the Physics of Electron Beam
Interactions to Determine Optimal Sampling and Image Reconstruction
Strategies for High Resolution STEM. 11 FUNNL: Fast Nonlinear Nonnegative
Unmixing for Alternate Energy Systems. 12 Structure Prediction from
Scattering Profiles: A Neutron-Scattering Use-Case. 13 Physics-Infused
Learning: A DNN and GAN Approach. 14 Combining System Modeling and Machine
Learning into Hybrid Ecosystem Modeling. 15 Physics-Guided Neural Networks
(PGNN): An Application in Lake Temperature Modeling. 16 Physics-Guided
Recurrent Neural Networks for Predicting Lake Water Temperature. 17
Physics-Guided Architecture (PGA) of LSTM Models for Uncertainty
Quantification in Lake Temperature Modeling, Index.