Knowledge Guided Machine Learning : Accelerating Discovery using Scientific Knowledge and Data. - Karpatne, Anuj; Kannan, Ramakrishnan; Kumar, Vipin, 1956- (Boca Raton, FL : CRC Press, 2023)
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
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.
Includes bibliographical references and index.
Keywords: Machine learning; Data mining; BUSINESS & ECONOMICS / Statistics; COMPUTERS / Computer Graphics / Game Programming & Design; COMPUTERS / Computer Science; Data mining; Machine learning