This three-year, $349,200 National Science Foundation project grant supports research to advance mathematical and computational modeling capabilities for quantifying uncertainties in coastal hazard simulations. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the University of Texas at Austin will lead efforts to develop and apply a data-to-distribution pipeline using deep learning techniques, scalable data-consistent inversion approaches, and iterative methods for...
This National Science Foundation (NSF) Project Grant award, under the Geosciences program (CFDA 47.050), provides $346,290 to the University of California San Diego's Scripps Institution of Oceanography to develop new computational tools for geophysical data analysis and uncertainty quantification. The project aims to generate models that can accurately reproduce sharp contrasts in geophysical properties, as well as quantify uncertainties in geophysical inversions. The research will leverage...
This National Science Foundation project grant of $179,999 will support computational modeling to characterize uncertainty in future coastal risk through August 2024. Funded under the Mathematical and Physical Sciences program, the award to Rochester Institute of Technology aims to examine how coastal areas can mitigate damages from sea-level change and coastal flooding. The grantee will develop software and conduct computer model experiments to assess uncertainty in geophysical and...
This $299,965 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences will allow Colorado State University (CSU) to develop and validate a new statistical model and analytical methods for assessing extremal dependence in high-dimensional data. This work aims to improve quantification of joint risks in applications such as finance, insurance, and climate science. The project will include training a graduate student in extreme value analysis techniques...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $220,000 Project Grant to the International Computer Science Institute (ICSI), a non-profit research organization, under the Mathematical and Physical Sciences program (CFDA 47.049). The project aims to develop resilient and reliable deep learning methods for forecasting complex spatiotemporal ground motion data, with applications in seismology, earth sciences, and other domains. Key technical objectives include...
This $319,964 Project Grant award from the National Science Foundation's Geosciences program (CFDA 47.050) supports collaborative research by Northeastern University to study how human engineering decisions impact delta morphodynamics and coastal systems over centuries. The research integrates novel numerical and computational modeling approaches, including agent-based modeling, dynamical system modeling, and participatory modeling, to understand how local-scale engineering interventions can...
The National Science Foundation (NSF) awarded a $253,946 Project Grant to the University of Florida (UF) under the Geosciences program (CFDA 47.050) to enhance the accessibility of novel geostatistical inversion workflows for cryosphere research. The project aims to create freely available geostatistics software with novel methods for combining physical and statistical information to enable fast, scalable simulations that account for spatial and physical constraints. This software will be...
This $125,000 Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program will support research at the University of Texas at Austin to develop a Bayesian inference framework for learning earthquake cycle deformation processes across scales. The project aims to create an advanced framework capable of assimilating multi-modal observational data into high-resolution forward models to infer...
This $550,000 National Science Foundation Project Grant under the Geosciences program (CFDA 47.050) will support the development of data-driven physics-informed machine learning models to predict flood-induced flow and sediment dynamics. Over a four-year period ending June 2027, the grantee will improve existing high-fidelity numerical modeling tools and apply them to evaluate flood impacts on infrastructure stability in large waterways. They will then use simulation results to inform and...
This $803,970 project grant from the National Science Foundation Office of Advanced Cyberinfrastructure will support the development of a scalable real-time streaming analytics and machine learning framework for geoscience and hazards research. A collaboration between the University of Colorado, University of Oregon, Rutgers University, and UNAVCO will create a data framework to enable generalized real-time streaming analytics and machine learning using over 1,500 sensors from the EarthScope and...