Project Grant 2423215
- The National Science Foundation (NSF) awarded a $569,490 Project Grant under the Computer and Information Science and Engineering (CISE) program to Michigan Technological University on September 1, 2025. The goal of this 5-year project is to develop a Bayesian symmetry-respecting machine learning framework to accelerate the prediction of electronic structures in materials design. The research aims to address key challenges in current machine learning models, such as uncertainty quantification,...
- This $375,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to accelerate the discovery, design, and implementation of new engineered photonic materials, particularly photonic metamaterials, through a data-driven deep learning approach. The project, led by the Georgia Tech Research Corporation, will establish deep learning frameworks to construct photonic metamaterials, integrate information on tailorable optical...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program, with CFDA Number 47.070, will support a $599,963 research project by the University of Southern California (USC) from January 1, 2025 to December 31, 2027. The project will explore a new mathematical lens based in combinatorics, optimization, and graph theory to deepen the understanding of machine learning and guide the development of improved algorithms. The...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $750,000 to Northeastern University to develop innovative engineered photonic materials using a data-driven deep learning approach. The research aims to accelerate the discovery, design, and implementation of new photonic metamaterials with tailored optical properties for applications in areas such as lasers, optical communications, quantum computing, and...
- The National Science Foundation awarded a $499,979 project grant to the George Washington University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research towards developing high-performance machine learning techniques on graphs from October 1, 2021 to September 30, 2024. The Computer and Information Science and Engineering program aims to advance computing and informatics research and education. This award will further those goals by...
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) is focused on enhancing machine learning with graph-structured data. The research aims to address the challenge of data distribution shifts in AI models when applied to real-world scenarios, particularly in fields like particle physics and biochemistry. The key activities under this 3-year award include: Developing methods to estimate and...
- 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....
- This $375,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support a collaborative research effort to accelerate the design and development of engineered photonic materials using a data-driven deep learning approach. The project aims to establish deep learning frameworks to construct photonic metamaterials with tailored optical properties, integrate information on constituent material platforms into deep...
- The National Science Foundation (NSF) awarded a $600,000 project grant under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the Regents of the University of Michigan to support research on reinforcement learning and transformer-inspired approaches for smart photonics inverse design. The project aims to enable non-experts to use artificial intelligence models to design sophisticated photonic structures for optical applications, while also advancing...
- This $300,000 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, awarded on April 1, 2025, aims to develop a foundation model for predicting rare events in atomistic simulations. The research, conducted by the University of Maryland, College Park, leverages advanced AI techniques like equivariant transformers, generative models, and multimodal learning to enhance prediction accuracy and generalization across...
This $199,040 Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop new classes of computational algorithms that combine the benefits of direct computer simulations and the speed of machine learning predictions. The project, titled "XTRIPODS: HYBRID SCIENCE-MACHINE LEARNING SOLVERS FOR NANOPHOTONICS AND METAMATERIALS," will embed scientific knowledge into the machine learning process to address the slow adoption of machine learning in computational sciences due to the lack of diverse training datasets. The collaborative research will focus on using linear partial differential Maxwell equations in electromagnetic composites to predict electromagnetic field distributions, with the potential to apply the methods to other computational science fields like acoustics and quantum science. The project will also serve as a training ground for interdisciplinary scientists working at the interface of physics, engineering, and computer science. No sub-awards are planned for this grant, which has an award date of September 1, 2024 and a completion date of August 31, 2026.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $199.0k | 7/8/24 |