Project Grant 2407033

Award Date 8/1/24
Completion Date 7/31/27
Dollars Obligated $188K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Tucson, AZ 85721, USA
Similar Awards
This $310,000 Project Grant was awarded on May 1, 2025 by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The project, titled "CDS & E: LEARNING EXPLAINABLE MICROSTRUCTURE REPRESENTATIONS FOR MATERIAL PROPERTY PREDICTION AND DESIGN," will be conducted by Arizona State University (ASU) over 3 years through April 30, 2028. The project aims to develop new computational algorithms that can efficiently represent and...
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $350,000 to the University of Texas at Austin to develop accurate mathematical models and computer simulations for non-equilibrium systems with memory effects. The research aims to address complex scientific phenomena across diverse domains, including biosystems, plasma evolution, and solar energy, by designing novel computational approaches that integrate...
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences (MPS) program provides $306,369 in funding to the University of Delaware to conduct computational modeling and education research on the multiscale simulation of nanofluid assembly for the design of smart materials. The research aims to gain a fundamental understanding of how microscale self-assembly processes in particle-stabilized multiphase fluids can be used to control the mesoscale...
This $273,291 National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences (CFDA 47.049) program will support the development of computational tools that combine machine learning and scientific computing for exploring and predicting polymer systems. The goal is to accelerate the discovery of new materials and facilitate the design of polymers and polymer systems with highly-tuned properties. Key products and services include: Developing efficient...
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) will fund research at Carnegie Mellon University (CMU) on mean-field and singular limits of deterministic and stochastic interacting particle systems. The $187,382 award, with a performance period from July 1, 2023 to May 31, 2025, aims to achieve a substantial reduction in computational complexity for modeling the behavior of large numbers of interacting particles,...
The University of Arizona was awarded a $334,752 project grant from the National Science Foundation Division of Mathematical Sciences. The grant falls under the Mathematical and Physical Sciences program (CFDA 47.049) and will support research into novel methods for numerical simulation of wave propagation in inhomogeneous media from September 1, 2021 to August 31, 2024. The funding will allow the University of Arizona to develop new computational techniques for modeling how waves, such as...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports computational and theoretical research at New York University (NYU) to quantify the complexity of materials landscapes. The $280,000 award, spanning March 1, 2025 to February 28, 2030, aims to develop methods for enumerating possible material states and calculating their probabilities. The research will elucidate relationships between structural regularities and...
The National Science Foundation (NSF) awarded a $560,337 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Arizona. The grant will develop computational simulation tools to investigate the physical, evolutionary, and observational consequences of convective mixing in gas giant planet atmospheres. The research aims to enhance understanding of how convective processes impact the structure and cooling of exoplanets and brown dwarfs....
This $296,555 Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support the development of reduced basis enhancements for neural networks and their application to quantum materials simulation. Specifically, the University of Massachusetts Dartmouth will combine traditional reduced basis methods with deep learning techniques to build an analysis-driven computational emulator for parameterized partial differential equations....
The National Science Foundation (NSF) awarded a $142,387 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Florida to develop new computational methods for describing strongly correlated electronic systems. The goal is to create accurate, efficient, and universal techniques that can model the complex electronic behavior found in molecules and materials with multiple unpaired electrons, such as rare-earth metals. The project has three key...

The National Science Foundation (NSF) awarded a $187,593 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Arizona, doing business as the Arizona Board of Regents. This 3-year effort, starting on August 1, 2024, seeks to develop novel modeling and simulation approaches for complex material systems. Key objectives include:

  • Utilizing machine learning, mathematical techniques, and physical principles to construct models that provide more realistic descriptions and improved predictive capabilities for physical systems.
  • Investigating energy-driven models of interacting particles and their mean-field limits using numerical simulation and rigorous analysis, with applications in areas like phase separation and self-assembling patterns.
  • Advancing graduate student training as an integral component of the project.

The research will explore applications in complex polymer systems, phase field models, model reduction, and materials data reconstruction. Overall, this project aims to develop new computational tools and modeling paradigms that can be adapted to a wide range of physical and biological systems, while also enhancing the university's research capabilities and supporting the education of future scientists.

Generated 3/4/25, 2:19 AM