Unsupervised and Physics-Informed Machine learning in Geosciences
V.V. Vesselinov, et al.
Baylor University, Texas2021
Summary
Our work explores the integration of unsupervised and physics-informed machine learning techniques in geosciences, highlighting applications and case studies related to water resource management, drought prediction, and geothermal resource assessment. We demonstrate how these approaches can extract meaningful patterns from complex geoscience datasets, improve model predictions, and support informed decision-making in the context of environmental challenges and resource management.