Synopsis:

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-a-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.

Sample Chapter PDFs:

• Chapter 1. Anuj Karpatne, Ramakrishnan Kannan, and Vipin Kumar, "Introduction"

• Chapter 3. Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Bryan Lewis, Adam Sadilek, Srinivasan Venkatramanan, and Madhav Marathe, Title: "Combining theory and data driven approaches for epidemic forecasts"

• Chapter 14. Markus Reichstein, Bernhard Ahrens, Basil Kraft, Gustau Camps-Valls, Nuno Carvalhais, Fabian Gans, Pierre Gentine, and Alexander J. Winkler, "Combining system modeling and machine learning into hybrid modeling"

Table of Contents:

• Chapter 1. Anuj Karpatne, Ramakrishnan Kannan, and Vipin Kumar, "Introduction" (Download PDF)

• Chapter 2. Steven L. Brunton and J. Nathan Kutz, "Targeted use of deep learning for physics and engineering"

• Chapter 3. Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Bryan Lewis, Adam Sadilek, Srinivasan Venkatramanan, and Madhav Marathe, Title: "Combining theory and data driven approaches for epidemic forecasts" (Download PDF)

• Chapter 4. Mojtaba Forghani, Yizhou Qian, Jonghyun Harry Lee, Peter Kitanidis, Matthew Farthing, Tyler Hesser, and Eric Darve, "Recent advances in machine learning and physics-based modeling in hydrology and geoscience"

• Chapter 5. Alexander Sun, Hongkyu Yoon, Zhi Zhong, and Chung Yan Shih, "Applications of physics-informed scientific machine learning in subsurface science: A survey"

• Chapter 6. Sifan Wang and Paris Perdikaris, "Adaptive training strategies for physics-informed neural networks"

• Chapter 7. Nicholas Geneva and Nicholas Zabaras, "Modern Deep Learning for Modeling Physical Systems"

• Chapter 8. Rui Wang, Robin Walters, and Rose Yu, "Physics-Guided Deep Learning for Spatiotemporal Forecasting"

• Chapter 9. Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh Tafti, and Anuj Karpatne, "Science-Guided Design & Evaluation of Machine Learning Models: A Case-Study on Multi-Phase Flows"

• Chapter 10. Nigel D. Browning, B. Layla Mehdi, Daniel Nicholls, and Andrew Stevens, "Using the Physics of Electron Beam Interactions to Determine Optimal Sampling and Image Reconstruction Strategies for High Resolution (S)TEM"

• Chapter 11. Jeffrey A. Graves, Thomas F. Blum, Piyush Sao, Miaofang Chi, and Ramakrishnan Kannan, "FUNNL: Fast Nonlinear Nonnegative Unmixing for Alternate Energy Systems"

• Chapter 12. Cristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Sudip K. Seal, "Structure Prediction from Scattering Profiles: A Neutron-Scattering Use-Case"

• Chapter 13. Zhibo Zhang, Ryan Nyugen, Souma Chowdhury, and Rahul Rai, "Physics-Infused Learning: A DNN and GAN Approach"

• Chapter 14. Markus Reichstein, Bernhard Ahrens, Basil Kraft, Gustau Camps-Valls, Nuno Carvalhais, Fabian Gans, Pierre Gentine, and Alexander J. Winkler, "Combining system modeling and machine learning into hybrid modeling" (Download PDF)

• Chapter 15. Arka Daw, Anuj Karpatne, Jordan Read, William Watkins, and Vipin Kumar, "Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling"

• Chapter 16. Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach, and Vipin Kumar, "Physics Guided Recurrent Neural Networks for Predicting Lake Water Temperature"

• Chapter 17. Arka Daw, R. Quinn Thomas, Cayelan C. Carey, Jordan Read, Alison P. Appling, and Anuj Karpatne, "Physics-Guided Architecture (PGA) of LSTM Models for Uncertainty Quantification in Lake Temperature Modeling"