Project Grant 2510830
- This $200,000 Project Grant was awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The award supports a collaborative research project to develop novel mathematical theories and computational methods for efficiently solving high-dimensional partial differential equations (PDEs) and learning solution operators using deep neural network-based approaches. The key objectives are to: 1) propose a supervised learning...
- This $597,791 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support collaborative research at Duke University to explore the synergies between machine learning and partial differential equations (PDEs). The research aims to strengthen the use of machine learning methods, specifically neural networks, for improving PDE solving processes, as well as to further elucidate the role of PDEs in...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $400,000 Project Grant to Duke University on August 1, 2023 under the Mathematical and Physical Sciences program (CFDA 47.049) to support innovative numerical methods for solving high-dimensional partial differential equations (PDEs). The key objectives of the 3-year project are to: (1) design and analyze neural-network parametrization for high-dimensional functions with symmetry constraints, and (2) develop and...
- This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences (CFDA 47.049) program provides $140,889 to Texas A&M University to conduct research connecting machine learning and numerical methods for partial differential equations. The key objectives are to leverage deep learning techniques to improve numerical methods for PDEs, and apply the theoretical understanding of finite element methods to better comprehend the success of deep neural networks....
- The National Science Foundation (NSF) awarded a $299,998 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to Georgia Tech Research Corporation to conduct fundamental research on the dynamics of nonlinear partial differential equation (PDE) systems that model fluid flow and nonlinear waves. The research will focus on analyzing the local dynamics near steady states in incompressible fluid PDEs with free surfaces, as well as a class of nonlinear...
- This National Science Foundation (NSF) project grant under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) was awarded to Carnegie Mellon University in the amount of $600,000 on August 15, 2024. The project will build mathematical foundations for using machine learning methods, specifically neural networks, to improve the process of solving partial differential equations (PDEs) and leverage PDEs as a tool for generative modeling. The research will explore issues...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $107,860 Project Grant to the Regents of the University of Minnesota, Office of Sponsored Projects Administration, a non-profit 1862 land grant college, to conduct research under the NSF Mathematical and Physical Sciences program (CFDA 47.049). The research project will develop theoretical foundations for using machine learning methods to solve high-dimensional partial differential equations, emphasizing predictive...
- This Project Grant award of $494,628 from the National Science Foundation (NSF) Division of Mathematical Sciences to Brown University supports the development of effective computational tools and rigorous theoretical foundations for using neural networks to numerically solve partial differential equations (PDEs). The project aims to address the key challenges of ensuring accuracy, reliability, and intelligibility of neural network-based approaches for scientific computing applications, where the...
- This $399,583 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research to develop numerical algorithms that can estimate solutions to partial differential equations (PDEs) without full boundary condition information. The research aims to enable improved modeling and forecasting capabilities across various applications, including meteorology, biology, and engineering design. The primary awardee, Texas...
- This Project Grant award of $150,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research into the asymptotic behavior of partial differential equations (PDEs) across a range of scientific applications. The Principal Investigator (PI) will investigate long-term propagation and front structure in reaction-diffusion equations, analyze limits of stochastic PDEs in physical systems, and study the effects of viscosity on shock...
This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) is focused on developing novel mathematical theories and computational methods to efficiently solve high-dimensional partial differential equations (PDEs) and learn PDE solution operators using deep neural network-based approaches. The $100,000 award to the Georgia State University Research Foundation Inc. will support research across three key objectives: (1) supervised learning for solving high-dimensional Hamilton-Jacobi equations using a density coupling strategy, (2) a parameter control framework for rapid simulation of high-dimensional evolution PDEs, and (3) a deep tangent bundle method for efficient high-dimensional function approximation and PDE simulation. This work aims to advance scientific computing capabilities for solving challenging PDE problems, while also providing training opportunities for the next generation of applied and computational mathematicians and engineers.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $100.0k | 8/14/25 |