Probabilistic Knowledge-guided Machine Learning in Engineering and Geoscience Systems

Abstract

Machine learning (ML) models have achieved significant success in commercial applications and have driven advancements in scientific discovery across many scientific disciplines. ML modeling has been essential in tackling complex scientific problems, often enhancing our understanding of previously poorly understood processes. These models have been developed to improve computational efficiency in scenarios where traditional process-based or mechanistic models provided only simplified approximations of physical processes. Despite their success, even state-of-the-art ML models can produce physically inconsistent predictions and have limited generalization capabilities. Additionally, the black-box nature of ML models means that researchers and stakeholders often lack insight into their reliability. This thesis proposes the development of novel Probabilistic Knowledge-Guided Machine Learning (P-KGML) models to address these concerns. P-KGML models integrate domain knowledge and probabilistic reasoning to improve the explainability, generalization, and physical consistency of ML outputs. These models are particularly valuable in engineering and geoscience systems, where understanding uncertainty and ensuring adherence to physical laws are crucial.