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Knowledge-guided Machine Learning: Current Trends and Future Prospects
March 26, 2024, 4:41 a.m. | Anuj Karpatne, Xiaowei Jia, Vipin Kumar
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv comparison cs.ai cs.ce cs.lg current future introduction knowledge machine machine learning modeling overview paper process prospects research scientific state trends type
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