Project Grant 2529303
- This $997,770 project grant, awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, aims to improve the accuracy and predictive capabilities of cardiac digital twin models for assessing the risk of sudden cardiac death. The research team at The Johns Hopkins University will develop novel, scalable, and data-driven mathematical and algorithmic calibration tools that combine advanced computational techniques and machine learning to...
- This $200,000 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program will fund the development of advanced computational methods to create high-fidelity, fast-running digital twins of patient hearts and cardiovascular medical devices. The project aims to deliver: 1) novel machine learning algorithms for accurate digital twin geometry reconstruction from 3D medical images, 2) an efficient inverse method to identify in vivo...
- This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) aims to develop a personalized digital twin technology that will enhance the monitoring and understanding of cardiovascular aging. The $250,000 award to Lehigh University will integrate data from wearable devices, echocardiographic measurements, and advanced cardiovascular modeling to create a physics-based...
- This federal Project Grant award for $444,202, provided by the National Science Foundation's (CFDA 47.049) Mathematical and Physical Sciences program, supports research at the University of Utah to develop a novel mathematical and computational framework for dramatically accelerating the calibration and updating of digital twin models. Digital twins are virtual representations of physical systems that enable real-time simulation, monitoring, and prediction of their real-world counterparts,...
- This $455,825 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) was granted to Iowa State University of Science and Technology to develop a personalized digital twin technology for monitoring and understanding cardiovascular aging. The project aims to integrate data from wearable devices, echocardiographic measurements, and advanced cardiovascular modeling to enhance the monitoring and early detection of...
- This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $370,774 to the University of Houston System to develop an innovative framework for learning digital twins of human physiology. The goal is to create personalized, data-enabled digital models that can simulate glucose metabolism and help evaluate new treatments and technologies for type 1 diabetes management, without the risks of real-world trials. The research...
- This Federal Project Grant award, provided by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), supports the development of a first-principles informed, data-enabled predictive digital twin framework for human physiology. The $432,000 award to Arizona State University, a Hispanic-serving institution, will advance techniques for integrating real-world data into physics-based models to create personalized digital representations of human metabolic...
- This $760,046 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a computationally-efficient multiscale modeling framework that integrates machine learning and artificial intelligence to predict structural and functional changes in the heart due to disease progression. The project aims to build fundamental understanding of heart disease by combining techniques from...
- This $952,867 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program aims to develop a digital twin framework for virtual clinical trials. The University of Texas at Austin will lead this 3-year project to create computational models and virtual patient populations to enable high-throughput screening of novel therapeutic interventions, especially for cancer treatment. The key objectives...
- This Project Grant award, valued at $175,007, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The award supports the development of a new framework for digital twin modeling of Alzheimer's disease (AD), combining clinical data, biomedical research, and advanced computational methods to enable personalized medicine. The project aims to build a unified modeling framework for...
This Project Grant award for $594,001.00 was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) to the University of Utah. The project, titled "MATHEMATICAL UNDERPINNINGS OF POPULATION-BASED CARDIAC DIGITAL TWINS", aims to advance the science and application of cardiac digital twins (CDTs) by leveraging mathematical and statistical methods to enhance the trustworthiness of CDT simulations. Key objectives include developing a framework for assessing and constructing CDTs through statistical inference, optimization-based calibration, and uncertainty quantification, as well as new exploration-exploitation algorithms to improve CDT predictive capabilities. The project will then integrate these foundational advances into a new holistic CDT framework that incorporates models across cellular, tissue, and organ-level scales, leveraging multimodal clinical data. This work is intended to support the development of precision medicine for addressing cardiovascular diseases, a leading cause of death in the United States.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $594.0k | 8/5/25 |