In Phase II, the team plans to expand the framework beyond UC’s groundwater observatory by incorporating additional datasets, field sites and scientific applications. The broader goal is to create a scalable AI-enabled framework supporting research in critical minerals, geothermal energy, carbon and hydrogen storage, and other subsurface energy and environmental challenges.<\/p>\n
\u201cThis project is about transforming how scientists characterize and predict complex subsurface systems,\u201d Soltanian said. \u201cRather than analyzing geological information, geophysical measurements, field observations and numerical simulations separately, we are developing an integrated AI-enabled scientific workflow that brings these different sources of information together into a unified framework.<\/p>\n
\u201cOne of the most exciting aspects of the project is that we are developing a general scientific framework that can be adapted to many different subsurface challenges rather than addressing only a single application,\u201d he said.<\/p>\n
Tang will lead development of the project\u2019s extended-reality interface, allowing researchers to interactively explore the spatial datasets and model predictions generated by the AI-enabled workflow.<\/p>\n
\u201cMy primary role will be to develop an AI-enabled digital twin that allows scientists to interactively explore spatial data generated from surrogate predictions,\u201d Tang said.<\/p>\n
\u201cWe believe that extended reality and interactive environments are essential for keeping the human in the loop in the age of AI,\u201d Tang said.<\/p>\n