Advanced Computational Tools

Our ability to collect and create diverse Earth and environmental data – at scales of seconds to decades, and from microns to thousands of kilometers – far outpaces our ability to assimilate it, much less improve our predictive understanding of complex, multi-scale Earth system behavior. These data sample hydrological, biological, geochemical, geological, atmospheric, and geomechanical properties using a variety of platforms, including omic’, wellbore, fiber optic, surface geophysical, and UAV and satellite approaches. In the past several years, EESA scientists have launched several new projects and have greatly advanced data archiving, modeling, and machine learning (ML) tools to advance predictive understanding using such diverse data.

Examples aligned with EESA’s ‘Scale-aware data and simulation capability’ Cross-Cutting platform include mechanistic subsurface simulation capabilities that can take advantage of emerging exascale computers and that use adaptive mesh refinement methods to ‘telescope’ into a system where higher modeling resolution is required, as well as cyberinfrastructure to enable long-term preservation and public dissemination of multiscale, diverse data, along with rapid analysis and synthesis capabilities.

Examples aligned with EESA’s new Cross-cutting Technology of Machine Learning include methods to rapidly characterize the organization of a landscape, provide near-real time indications of hazards, and downscale forcing data. ML has also been used to advance hybrid modeling approaches that take advantage of both mechanistic models and data-driven approaches to extract insights and improve prediction of complex phenomena using diverse datasets. EESA scientists have developed and tested these approaches over the last few years for problems ranging from detection of extreme events, to quantifying ecosystem and watershed hydrobiogeochemical behavior to advancing understanding of subsurface geomechanics and flow. During 2019, EESA scientists co-chaired each of the four DOE ‘AI for Science’ Townhall Earth and Environmental sessions, which were held across different regions of the U.S.