Knowledge-Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data

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.