Knowledge-guided Machine Learning: Current Trends and Future Prospects
Anuj Karpatne Xiaowei Jia Vipin Kumar
This paper presents an overview of scientific modeling and discusses the
complementary strengths and weaknesses of ML methods for scientific modeling in
comparison to process-based models. It also provides an introduction to the
current state of research in the emerging field of scientific knowledge-guided
machine learning (KGML) that aims to use both scientific knowledge and data in
ML frameworks to achieve better generalizability, scientific consistency, and
explainability of results. We discuss different facets of KGML research in
terms of the type of scientific knowledge used, the form of knowledge-ML
integration explored, and the method for incorporating scientific knowledge in
ML. We also discuss some of the common categories of use cases in environmental
sciences where KGML methods are being developed, using illustrative examples in
each category.