Project Grant 2533956
- 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 $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 National Science Foundation (NSF) Project Grant award under CFDA 47.041 (Engineering) supports the development of a new computational model to predict the complex interactions between ventricular assist devices (VADs) and the beating native heart. The goal is to create a "digital twin" of an experimental mock circulatory loop used for testing VAD safety and efficacy. This $195,291 award, effective August 1, 2024 through July 31, 2026, will result in a lumped parameter model that...
- The National Science Foundation (NSF) awarded a 3-year, $567,284 Project Grant under the Engineering program (CFDA 47.041) to Purdue University to develop a scalable Bayesian methodology for reconstructing cardiovascular hemodynamic flow fields and cardiac structure from advanced medical imaging modalities like phase-contrast MRI, 4D flow MRI, and color Doppler echocardiography. The research aims to overcome limitations in current imaging techniques, such as inaccurate velocity flow...
- The National Science Foundation (NSF) awarded a $361,979 Project Grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) to Cornell University to develop computational cyberinfrastructure for data-enabled forward and inverse stochastic cardiovascular modeling. The project aims to establish a novel paradigm of data-augmented cardiovascular fluid-structure simulations to help transform personalized cardiovascular diagnostics and therapeutics....
- 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 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $439,954 to Michigan State University (MSU) to develop advanced computational models of the heart that leverage machine learning and artificial intelligence. The goal is to create a multiscale modeling framework that can predict structural and functional changes in the heart due to disease conditions like pathological fibrosis. The...
- This National Science Foundation (NSF) Engineering program (CFDA 47.041) project grant award of $650,000 supports research to advance the understanding of blood-clot interactions that may result in stroke for patients with heart rhythm disorders. Specifically, the award will enable the development of a computational model of the left atrium to investigate how the heart's structure changes blood flow patterns and elevates the risk of clot breakup, which can lead to stroke. The research will be...
- This Project Grant award of $506,803, provided by the National Science Foundation's (NSF) Engineering program (CFDA 47.041), will fund research by the Research Foundation for the State University of New York (RF SUNY) to develop advanced multiscale modeling capabilities for predicting thrombus (clot) formation and its response to external loads. The goal is to create computational tools that can better forecast clot growth and rupture under varying blood flow conditions, such as pulsatile...
- This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) provides $999,745 to Iowa State University to develop a novel multiscale digital twin framework for predicting and controlling blood vessel growth after cardiac injury. The key products and services to be delivered include: A multiscale modeling framework that integrates molecular signaling dynamics, cellular migration, sprouting patterns, and tissue-level growth and...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) provides $640,562 to Duke University to develop advanced computer models that act as "virtual replicas" of a patient's blood vessels and heart activity. Using data from wearable sensors, the investigators will simulate how blood flows and interacts with vessel walls over time, enabling large-scale in silico testing of medical devices and therapies without invasive procedures. This work aims to accelerate the evaluation of cardiovascular treatments, support earlier detection of heart problems, and enable more personalized care. The award runs from September 15, 2025 through August 31, 2028 and addresses the need for scalable, high-fidelity assessment of how anatomical variability influences hemodynamic biomarkers.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $640.6k | 8/20/25 |