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Knowledge Guided Machine Learning : Accelerating Discovery using Scientific Knowledge and Data
(Chapman & Hall/CRC data mining and knowledge discovery series)

Publisher	([Place of publication not identified] : Chapman and Hall/CRC)
Year	2022
Edition	First edition.
Authors	Karpatne, Anuj editor Kannan, Ramakrishnan editor Kumar, Vipin 1956- editor

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Links to the text	Location	Volume	Call No.	Barcode No.	Status	Comments	ISBN	Printed	Restriction	Reserve
Links to the text	Library Off-campus access			OB00190630		Taylor & Francis eBooks (電子ブック)	9781003143376

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Material Type	E-Book
Media type	機械可読データファイル
Size	1 online resource (xii, 430 pages)
Notes	About the EditorsList of Contributors1 IntroductionAnuj Karpatne, Ramakrishnan Kannan, and Vipin Kumar2 Targeted Use of Deep Learning for Physics and EngineeringSteven L. Brunton and J. Nathan Kutz3 Combining Theory and Data-Driven Approaches for Epidemic ForecastsLijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Bryan Lewis, Adam Sadilek, Srinivasan Venkatramanan, and Madhav Marathe4 Machine Learning and Projection-Based Model Reduction in Hydrology and GeosciencesMojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew Farthing, Tyler Hesser, Peter K. Kitanidis, and Eric F. Darve5 Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A SurveyAlexander Y. Sun, Hongkyu Yoon, Chung-Yan Shih, and Zhi Zhong6 Adaptive Training Strategies for Physics-Informed Neural NetworksSifan Wang and Paris Perdikaris7 Modern Deep Learning for Modeling Physical SystemsNicholas Geneva and Nicholas Zabaras8 Physics-Guided Deep Learning for Spatiotemporal ForecastingRui Wang, Robin Walters, and Rose Yu9 Science-Guided Design and Evaluation of Machine Learning Models: A Case-Study on Multi-Phase FlowsNikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh Tafti, and Anuj Karpatne10 Using the Physics of Electron Beam Interactions to Determine Optimal Sampling and Image Reconstruction Strategies for High Resolution STEMNigel D. Browning, B. Layla Mehdi, Daniel Nicholls, and Andrew Stevens11 FUNNL: Fast Nonlinear Nonnegative Unmixing for Alternate Energy SystemsJeffrey A. Graves, Thomas F. Blum, Piyush Sao, Miaofang Chi, and Ramakrishnan Kannan12 Structure Prediction from Scattering Profiles: A Neutron-Scattering Use-CaseCristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, and Sudip K. Seal13 Physics-Infused Learning: A DNN and GAN ApproachZhibo Zhang, Ryan Nguyen, Souma Chowdhury, and Rahul Rai14 Combining System Modeling and Machine Learning into Hybrid Ecosystem ModelingMarkus Reichstein, Bernhard Ahrens, Basil Kraft, Gustau Camps-Valls, Nuno Carvalhais, Fabian Gans, Pierre Gentine, and Alexander J. Winkler15 Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature ModelingArka Daw, Anuj Karpatne, William D. Watkins, Jordan S. Read, and Vipin Kumar16 Physics-Guided Recurrent Neural Networks for Predicting Lake Water TemperatureXiaowei Jia, Jared D. Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach, and Vipin Kumar17 Physics-Guided Architecture (PGA) of LSTM Models for Uncertainty Quantification in Lake Temperature ModelingArka Daw, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling, and Anuj KarpatneIndex Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML OCLC-licensed vendor bibliographic record HTTP:URL=https://www.taylorfrancis.com/books/9781003143376 Information=Taylor & Francis
Subjects	FREE:BUSINESS & ECONOMICS / Statistics FREE:COMPUTERS / Computer Graphics / Game Programming & Design FREE:COMPUTERS / Computer Science LCSH:Machine learning LCSH:Data mining
Classification	LCC:Q325.5 DC23:006.3/1
ID	8000089796
ISBN	9781003143376