This $300,000 federal Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop physics-guided generative artificial intelligence models for inverting chaotic advection-diffusion dynamics. The research aims to enable more accurate source identification from limited observations of complex physical processes like pollution transport, virus spread, and wildfire evolution, which are...
This $399,162 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop interpretable, stable, and mass-conserving artificial intelligence (AI) models to improve the computational speed and efficiency of geoscientific models, such as those used for air pollution and climate research. The project will create simpler "surrogate" machine learning models for key components like atmospheric chemistry and wildfire plume rise, allowing for...
This $250,000 project grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of California, Los Angeles (UCLA). The project aims to develop generative artificial intelligence (AI) frameworks to aid scientific reasoning and accelerate sustainable development. Specifically, the grant will fund the creation of new generative AI architectures, objectives, and techniques to efficiently...
This $207,737 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop a new class of machine learning models called "Programmatic Foundation Models" that can efficiently analyze large-scale satellite, aerial, and ground imagery. The goal is to create interpretable, robust AI models that can understand global and local phenomena from images, providing insights...
This National Science Foundation (NSF) Project Grant award under the Geosciences Program (CFDA 47.050) provides $300,000 in funding to the Massachusetts Institute of Technology (MIT) from November 15, 2024 to October 31, 2027. The award supports the development of machine learning-powered "surrogate models" to increase the computational speed and efficiency of geophysical models used for air pollution and climate research. Key project objectives include: Creating simplified,...
This $350,000 federal Project Grant was awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program. The goal of the research is to develop accurate mathematical models and computer simulations for studying non-equilibrium systems with memory effects, such as those found in biosystems, plasma evolution, and solid-state nanostructures. The Principal Investigator will focus on analytical and numerical approaches to statistical transport...
This $253,337 federal Project Grant award, funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research by the University of Maryland, College Park to develop new capabilities for monitoring and understanding forest carbon dynamics in the Earth system. The key products and services to be delivered include: 1) cross-platform and cross-region learning frameworks to enable fine-scale carbon dynamics monitoring...
This $477,585 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research to develop new methods, algorithms, and software that integrate machine learning/artificial intelligence (ML/AI) with traditional physical knowledge in "physics-informed machine learning" (PIML) models. The project aims to create a cyberinfrastructure that enables the seamless and synergistic integration of ML/AI with...
This Project Grant award of $500,000.00 from the National Science Foundation's Geosciences Program (CFDA 47.050) is funding the development of a transformative AI-based framework for generating high-fidelity, physically consistent, and uncertainty-calibrated geoscience data. The goal is to overcome limitations in observational infrastructure and computational cost to produce enhanced datasets that can improve decision-making for disaster preparedness, emergency response, and infrastructure...
This EAGER (Early-Concept Grants for Exploratory Research) award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will develop generative artificial intelligence (AI) methods to learn from computational physics simulations and mathematical equations. The $300,000 project aims to expand the capabilities of large language models, such as OpenAI's ChatGPT and Microsoft's Copilot, to go beyond text-based learning and make predictions on complex, coupled physics problems...