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.


  • 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


chapter Chapter 1|30 pages


ByAnuj Karpatne, Ramakrishnan Kannan, Vipin Kumar

chapter Chapter 2|24 pages

Targeted Use of Deep Learning for Physics and Engineering

BySteven L. Brunton, J. Nathan Kutz

chapter Chapter 3|28 pages

Combining Theory and Data-Driven Approaches for Epidemic Forecasts

ByLijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Bryan Lewis, Adam Sadilek, Srinivasan Venkatramanan, Madhav Marathe

chapter Chapter 4|28 pages

Machine Learning and Projection-Based Model Reduction in Hydrology and Geosciences

ByMojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve

chapter Chapter 5|22 pages

Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey

ByAlexander Y. Sun, Hongkyu Yoon, Chung-Yan Shih, Zhi Zhong

chapter Chapter 6|28 pages

Adaptive Training Strategies for Physics-Informed Neural Networks

BySifan Wang, Paris Perdikaris

chapter Chapter 7|18 pages

Modern Deep Learning for Modeling Physical Systems

ByNicholas Geneva, Nicholas Zabaras

chapter Chapter 8|32 pages

Physics-Guided Deep Learning for Spatiotemporal Forecasting

ByRui Wang, Robin Walters, Rose Yu

chapter Chapter 9|22 pages

Science-Guided Design and Evaluation of Machine Learning Models: A Case-Study on Multi-Phase Flows

ByNikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh Tafti, Anuj Karpatne

chapter Chapter 10|28 pages

Using the Physics of Electron Beam Interactions to Determine Optimal Sampling and Image Reconstruction Strategies for High Resolution STEM

ByNigel D. Browning, B. Layla Mehdi, Daniel Nicholls, Andrew Stevens

chapter Chapter 11|26 pages

FUNNL: Fast Nonlinear Nonnegative Unmixing for Alternate Energy Systems

ByJeffrey A. Graves, Thomas F. Blum, Piyush Sao, Miaofang Chi, Ramakrishnan Kannan

chapter Chapter 12|18 pages

Structure Prediction from Scattering Profiles: A Neutron-Scattering Use-Case

ByCristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Sudip K. Seal

chapter Chapter 13|22 pages

Physics-Infused Learning: A DNN and GAN Approach

ByZhibo Zhang, Ryan Nguyen, Souma Chowdhury, Rahul Rai

chapter Chapter 14|26 pages

Combining System Modeling and Machine Learning into Hybrid Ecosystem Modeling

ByMarkus Reichstein, Bernhard Ahrens, Basil Kraft, Gustau Camps-Valls, Nuno Carvalhais, Fabian Gans, Pierre Gentine, Alexander J. Winkler

chapter Chapter 15|20 pages

Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

ByArka Daw, Anuj Karpatne, William D. Watkins, Jordan S. Read, Vipin Kumar

chapter Chapter 16|26 pages

Physics-Guided Recurrent Neural Networks for Predicting Lake Water Temperature

ByXiaowei Jia, Jared D. Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach, Vipin Kumar

chapter Chapter 17|18 pages

Physics-Guided Architecture (PGA) of LSTM Models for Uncertainty Quantification in Lake Temperature Modeling

ByArka Daw, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling, Anuj Karpatne