Project Grant 2152717
- This three-year project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) provides $200,000 to support research at the University of Utah developing new deep learning algorithms for sequential and graph data motivated by differential equations theory. The grant aims to advance machine learning methodology for ubiquitous problems involving sequentially observed data from multiple agents, such as pandemic spread modeling, cooperative robotics...
- This National Science Foundation (NSF) Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) will provide $295,000 to the University of California, San Diego (UCSD) from August 1, 2024 to July 31, 2027. The grant will support research focused on developing a broad theoretical framework and general scaling limit theory for high-dimensional stochastic differential equations. The research aims to advance the understanding of how the behavior of systems described by...
- This $307,266 Project Grant, awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program, aims to develop effective computational methods for training neural networks. The project focuses on establishing a novel Exploration-Exploitation-Determination (EED) framework to improve the training performance of neural networks, which are a core component of modern artificial intelligence (AI) models. Key objectives include:...
- The National Science Foundation (NSF) awarded a $800,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Los Angeles (UCLA) - Office of Research Administration. The grant, titled "COLLABORATIVE RESEARCH: III: MEDIUM: VIRTUALLAB: INTEGRATING DEEP GRAPH LEARNING AND CAUSAL INFERENCE FOR MULTI-AGENT DYNAMICAL SYSTEMS", will fund the development of a virtual laboratory framework to model and predict the...
- This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $399,998 to the University of Texas at Austin to develop novel algorithms that integrate classical numerical schemes and deep learning to address complex scientific computing challenges. The project aims to tackle problems in high-dimensional, nonlinear differential equations, long-time simulation of Hamiltonian systems, and boundary integral equations. The...
- 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 $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...
- This $299,889 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research at the University of California, San Diego (UCSD) to develop algorithms for compressing and improving the efficiency of large neural networks used in modern artificial intelligence applications. The key products and services to be delivered include: The research project focuses on developing quantization, pruning, and low-rank...
- This National Science Foundation (NSF) Project Grant award for $200,000.00, under the Mathematical and Physical Sciences program (CFDA 47.049), aims to develop 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 key objectives include a supervised learning method for solving high-dimensional Hamilton-Jacobi equations, a parameter control...
This National Science Foundation award provides $399,998 to the University of California, Los Angeles under the Mathematical and Physical Sciences program (CFDA 47.049) for the period of July 1, 2022 through June 30, 2025. The project will develop new algorithms and mathematical theory for multi-agent sequential deep learning using insights from ordinary and partial differential equations. Researchers will integrate advances in neural ordinary differential equations with graph networks to build next-generation algorithms based on wave equations on graphs. The research aims to overcome over-smoothing issues in sequential learning on graphs with deep architectures. It is anticipated the results and algorithms developed through this work will have broad applicability in areas such as pandemic spread modeling, cooperative robotics, and environmental change analysis.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $200.0k | 6/23/22 |