Project Grant 2211908
- This $1,197,878 project grant from the National Science Foundation's Office of Advanced Cyberinfrastructure will support the development of Evolutional Deep Neural Network algorithms for solving high-dimensional partial differential equations. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), this collaboration between U.S. and French researchers aims to accelerate computational predictions of complex phenomena across multiple disciplines. Specifically, the...
- 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) awarded a $599,474 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Santa Barbara (UCSB) to develop a real-time and energy-efficient neural network-based Partial Differential Equation (PDE) solver on a 2.5D photonic chip. The goal is to create a highly compressed and backward propagation-free training method for large-scale Physics-Informed Neural Networks (PINNs) that can be...
- The University of California, San Diego (UCSD) was awarded a $455,058 project grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The 4-year project, which started on July 1, 2024, focuses on developing techniques for a mathematical understanding of deep learning and its application to a variety of neural network models and data sets. The key objectives are to: 1) compare different networks and understand...
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at the University of California, San Diego (UCSD) to develop theoretical frameworks and computational methods for reconstructing complex 3D shapes using neural implicit representations. The key objectives are to enable the reconstruction of 3D shapes with intricate topologies, such as objects with holes, and to allow...
- This project grant award of $600,000 from the National Science Foundation's (NSF) Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering (CFDA #47.070) program will support the development of hybrid models that combine deep neural networks and high-fidelity partial differential equation (PDE) solvers. The goal is to create a system that maintains the accuracy of PDE models while leveraging the speed of neural networks to enable accelerated solutions for...
- The National Science Foundation Office of Advanced Cyberinfrastructure awarded the University of Texas at Austin a $1.2 million Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) from September 1, 2022 to August 31, 2025. The grant funds research to develop a rigorous and reliable scientific deep learning framework for forward, inverse, and uncertainty quantification problems in computational science and engineering. Specific objectives include...
- The University of California Irvine was awarded a $389,999 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The grant will support research into computational methods for learning and generating stochastic particle representations of mathematical models in physics and biology. Specifically, the university will develop a new class of computational tools integrating particle...
- 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...
- 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...
This $1.2 million Project Grant from the National Science Foundation Office of Advanced Cyberinfrastructure, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), will support the development of a geometry-aware and deep learning-based cyberinfrastructure for scalable modeling of solids and fluids at the University of California, Irvine from June 2022 to May 2025. The university will build a library of deep neural networks trained to solve single-physics partial differential equation systems over small domains, called "genomes," and will develop an adaptive method to couple the neural networks to solve multi-physics problems over large domains while satisfying the governing equations across the entire area. This "LEGO" framework is intended to enable on-the-fly approximation of solid and fluid behavior using pre-trained networks, eliminating long training times while increasing accuracy and scalability.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 6/15/22 |