Project Grant 2309378
- 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 $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 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...
- 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....
- 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 Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) provides $236,282 to the University of Kansas Center for Research Inc. to study the dynamics of partial differential equations (PDEs) in bounded domains. The project will yield a universal methodology for analyzing PDEs in one and higher spatial dimensions, addressing fundamental questions about the existence, uniqueness, and stability of solutions. It will...
- This $399,998 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences, under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program, aims to develop new nonlinear solution methods for solving systems of partial differential equations (PDEs). The proposed research seeks to create "lifted Newton nonlinear preconditioning" techniques that can improve the convergence of iterative solvers for severely nonlinear PDE problems, which...
- 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...
- The National Science Foundation (NSF) awarded a $420,000 Project Grant under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) to the University of Maryland, College Park for the project "Nonlinear Geometric PDEs: Modeling, Analysis and Approximation". The objective of this 3-year grant, awarded on August 1, 2025 and running through July 31, 2028, is to develop predictive computational tools for modeling, analyzing, and approximating nonlinear geometric partial...
- The National Science Foundation (NSF) awarded a $689,835 Project Grant under its Mathematical and Physical Sciences (CFDA 47.049) program to Brown University. The grant, with a performance period from December 1, 2024 to November 30, 2027, supports research focused on developing machine learning approaches for solving long-standing open problems in nonlinear partial differential equations, including dispersive, elliptic, and geometric frameworks. The project aims to unlock new mathematical...
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 analyze efficient adaptive sampling strategies for training neural-network solutions to high-dimensional PDEs. The project aims to advance mathematical understanding and improve numerical algorithms for quantum many-body problems and other high dimensional PDE challenges through cross-fertilization of mathematical analysis and numerical algorithm development. The project also involves new curriculum development and training of graduate students in applied mathematics and computational science.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $400.0k | 7/19/23 |