Deep Learning in Earth and Environmental Science

Catalog description

Machine learning (ML) has become an increasingly important tool for improving predictive and diagnostic models. It is also being adopted across a multitude of physical disciplines. However, in most physical science applications (e.g., seismology and geophysics, climate and atmospheric science, environmental and ecosystem science), researchers are interested in doing more than merely improving their models of real-world phenomena: we are interested in using the broader family of AI tools to deepen our understanding of the dynamic, physical or ecological processes that are fundamental to these fields. This course will explore deep learning, how machine learning relates to first principles physical models in a wide range of geological and geophysical sciences, atmospheric sciences, environmental sciences, and hydrogeology/hydrology.