Project Grant DESC0024937

This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) provides $650,000 over a 3-year period to the University of Southern California to develop an AI model that can better understand fault dynamics and earthquake hazards in heavily faulted geologic basins. The project builds a multiphysics fault network model to discover reduced-order governing equations for the evolution of stress in complex fault systems, using the Southern Permian Basin in the...

This National Science Foundation (NSF) Engineering grant award to Cornell University, with a total funding of $412,236, will deploy artificial intelligence (AI) strategies to model reactive flow and rock weathering processes across multiple spatial and temporal scales. The key objectives are to: (1) construct a database of virtual reactive flow experiments; (2) train a deep convolutional neural network to identify microstructural features linked to field variable variations; (3) enhance...

This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $692,942 in funding to the University of California, San Diego (UCSD) to develop an integrated experimental and computational framework for modeling the hydro-chemo-mechanical behavior of geomaterials across scales. The key objectives of this 5-year research project are: 1) Creating a machine learning-enabled multiscale material characterization framework for heterogeneous geomaterials,...

This $175,000 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports research to develop an AI model for understanding fault dynamics and earthquake hazards in complex fault networks. The project, led by the California Institute of Technology (Caltech), aims to build a multiphysics fault network model to discover reduced-order governing equations for the evolution of stress in fault systems. This work will enable improved assessment of regional...

This National Science Foundation (NSF) Project Grant under the Geosciences program (CFDA 47.050) will provide $392,914 to Carnegie Mellon University (CMU) from October 1, 2024 to September 30, 2027 to develop a collaborative research project titled "CAIG: Next Generation Machine-Learning Approach to Decode High-Resolution Earthquake Catalogs." The project aims to leverage advanced machine learning models and algorithms to gain deeper insights into earthquake dynamics and improve...

This National Science Foundation (NSF) Geosciences Program (CFDA 47.050) Project Grant award to the University of Southern California (USC) in the amount of $599,999 will support the development of novel physics-informed causal deep learning models (PINCER) to capture and predict subsurface flow and transport dynamics. The objective is to advance artificial intelligence (AI) techniques for modeling complex interactions among rocks, fractures, and fluids in subsurface systems, which impact...

This Project Grant from the National Science Foundation Division of Earth Sciences provides $399,904 to enhance discrete fracture network modeling using evolutionary and quantum computing approaches. Funded under the Geosciences program (CFDA 47.050), which supports basic research in atmospheric, earth, and ocean sciences, the award runs from September 1, 2021 to August 31, 2024. Through the Desert Research Institute, the primary awardee, and a subaward to the University of Nevada, Reno, the...

This $607,802 project grant, awarded by the National Science Foundation's Engineering program (CFDA 47.041), supports the development of a physics-informed machine learning framework for modeling the seismic response of geostructural systems. The key objectives are to: Develop scalable machine learning algorithms to accurately model the dynamic response of geostructural systems to seismic wave propagation. Develop approaches to accommodate uncertainties in uncertainty quantification tasks....

This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) totaling $225,816 will support collaborative research to investigate the evolution of elastic wave properties during the seismic cycle. The primary objectives are to explore whether variations in seismic wave properties can be identified before and/or after earthquakes, determine the underlying physical mechanisms, and assess the potential for operational forecasting of ruptures on meter-scale...

This Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences provides $250,000 to the University of Illinois in Urbana, Illinois over the period of August 15, 2023 to July 31, 2026. The goal of the project is to develop a rigorous mathematical formulation to describe fractures in dissipative solids, which can dissipate energy through viscous or plastic deformation. The key objectives are to: 1) Develop a time-discrete formulation of brittle fracture...