{"response":{"award":[{"abstractText":"Earthquakes, landslides, and floods cause hundreds of billions of dollars in damage and take thousands of lives each year. These hazards are often linked: heavy rain can saturate a hillslope, an earthquake can weaken that same slope, and the slope may then fail during a later storm. Forecasting where and when such events will strike remains hard, in part because the measurements that could warn of them, such as ground vibrations and deformation, satellite imagery, meteorological data, and groundwater levels, are gathered by separate systems that rarely work together. This project builds open shared computing tools that combine these streams of data with numerical simulations of the ground and water dynamics, producing a continuously updating digital twin of a region that can estimate hazards in close to real time. The investigators test the full system on landslides in Alaska and the Pacific Northwest. By making advanced computation and artificial intelligence (AI) simpler to use, the project lowers the adoption barriers for this kind of research for all scientists. The project also trains students through open tutorials, online workshops, and graduate courses. The same tools, built here for geophysical hazards, are designed to serve as a template for other fields that are rich in data and physical models. By helping protect communities from natural disasters and by broadening who can take part in computational science, the project advances scientific knowledge and the national welfare.\r\n\r\nThe Geophysical AI-driven Integration and Assimilation (GAIA) project develops a unified cyberinfrastructure that links physical simulations, sensor observations, and artificial intelligence across computing systems ranging from laptops to commercial cloud platforms and national high-performance computing centers. A team of researchers carries out five connected lines of work. First, the team is assembling curated, analysis-ready datasets that pair records of past landslides and ground failures with seismic, geodetic, and hydrologic sensor networks, with weather observations, and with satellite imagery that preceded and accompanied the landslides and ground failures. These observations are combined with matching physics simulations and a corpus of scientific literature to build a curated knowledge base. Second, the team builds a data aggregator, a unified software client that gathers data from many national archives and returns it in consistent, cloud-native formats ready for modeling across computing systems. Third, the team designs methods that fuse observations of differing spatial and temporal resolution, combining, for example, frequent measurements at fixed points with detailed but infrequent satellite maps. Fourth, the team trains fast surrogate models that approximate expensive physics simulations and couples them to incoming observations through data assimilation to produce real-time digital twins that forecast geohazards. Fifth, the team prototypes research-assisting software agents that retrieve and reason over selected literature and code to recommend methods, flag knowledge gaps, and generate reproducible workflows. The resulting open software, datasets, and trained models give the research community scalable methods for combining diverse data with physical models, an approach useful well beyond hazard science.\r\n\r\nThis award by the Office of Advanced Cyberinfrastructure is jointly supported by the Research, Innovation, Synergies and Education section in the Directorate of Geosciences.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF ALASKA FAIRBANKS","awardeeAddress":"2145 N TANANA LOOP","awardeeCity":"FAIRBANKS","awardeeCountryCode":"US","awardeeDistrict":"00","awardeeDistrictCode":"AK00","awardeeName":"University of Alaska Fairbanks Campus","awardeePhone":"9074747301","awardeeStateCode":"AK","awardeeZipCode":"99775","cfdaNumber":"47.050, 47.070","coPDPI":["Michael E West mewest@alaska.edu","Franz J Meyer fjmeyer@alaska.edu","Ronni Grapenthin rgrapenthin@alaska.edu"],"date":"07/10/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"1772170","expDate":"07/31/2032","fundAgencyCode":"4900","fundProgramName":"AI and Geosciences, Software Institutes","fundsObligated":["FY 2026 = $1,772,170.00"],"fundsObligatedAmt":"1772170","histAwd":"false","id":"2608510","initAmendmentDate":"07/10/2026","latestAmendmentDate":"07/10/2026","managingPec":"800400","orgCodeDir":"05000000","orgCodeDiv":"05090000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure (OAC)","orgUrl":"https://www.nsf.gov/div/index.jsp?div=OAC","parentUeiNumber":"KNP1HA2B9BF8","pdPIName":"Carl H Tape","perfAddress":"2145 N TANANA LOOP","perfCity":"FAIRBANKS","perfCountryCode":"US","perfDistrict":"00","perfDistrictCode":"AK00","perfLocation":"University of Alaska Fairbanks Campus","perfStateCode":"AK","perfZipCode":"99775","pi":["Carl H Tape ctape@alaska.edu"],"piEmail":"ctape@alaska.edu","piFirstName":"Carl","piId":"269812255","piLastName":"Tape","piMiddeInitial":"H","poEmail":"mpierce@nsf.gov","poName":"Marlon Pierce","poPhone":"7032927743","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"368Y00, 800400","program":"Artificial Intelligence (AI), Software Institutes, EXP PROG TO STIM COMP RES","progRefCode":"075Z, 8004, 9150","publicAccessMandate":"1","startDate":"08/01/2026","title":"Collaborative Research: Frameworks: Geophysical AI-driven Integration & Assimilation (GAIA)","transType":"Standard Grant","ueiNumber":"FDLEQSJ8FF63"}],"metadata":{"offset":0,"rpp":25,"totalCount":1}}}