Project Grant 2309403
- 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'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...
- 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...
- Federal Project Grant Award Summary The University of Michigan received a $500,000 project grant awarded June 15, 2025, from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The project, titled "ACED: A Unified Framework of Physics-Informed and Domain-Adapted Generative Diffusion Model for Efficient and Reliable Nanophotonics Inverse Design," will develop an...
- This $150,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) funds the development of an innovative computational electromagnetics (CEM) framework that leverages physics-informed artificial intelligence (AI) models. The project aims to enhance the analysis and design of on-chip optical interconnects, which are crucial for achieving ultra-high bandwidth and speeds in modern computing...
- This Project Grant award of $500,000.00 from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) to the University of Maryland, College Park will explore a new class of low-loss phase change materials to control light propagation and enable "synthetic dimensions" for advanced optical information processing and computing. The project aims to develop reconfigurable modal control and topological nanophotonics for next-generation communications and quantum computing...
- 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...
- This $400,000 Project Grant was awarded on July 1, 2024 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to the Regents of the University of Michigan to conduct collaborative research on the theoretical foundations of compositional learning in large language models based on transformer architectures. The research aims to investigate three key areas: model expressivity, statistical learning theory, and optimization, with...
- This $424,750 Project Grant awarded by the National Science Foundation's Engineering program (CFDA 47.041) supports the development of an ultrashort-pulse optical neural network system for brain-scale computing. The research aims to leverage the speed, bandwidth, and low-loss properties of light to enable real-time processing of billion-scale neural network models using minimal spatial elements. This approach seeks to overcome the limitations of current optical computing architectures. The...
- Grant Award Summary The University of Michigan received a $100,000 Project Grant from the National Science Foundation (NSF) Technology, Innovation, and Partnerships program (CFDA 47.084) on October 1, 2025, with performance completion targeted for September 30, 2029. This collaborative research initiative, titled "OCTICALLY-ACCELERATED HETEROGENEOUS AI COMPUTING CHIPLET (OCTANT)," advances integrated photonics technology to address critical computing bottlenecks in artificial...
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 fundamental knowledge in photonics. The research team plans to explore deep reinforcement learning and transformer methods to address the optical inverse design of large-scale and complicated nanostructures. The findings will be applied to experimental demonstrations, and the developed technologies are expected to become more effective as additional data becomes available for training the neural networks. The award period runs from October 1, 2023 to September 30, 2026.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 9/7/23 |