This $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of next-generation mathematical and algorithmic tools to address two key issues in applying machine learning to statistical modeling of time-evolving complex systems: a shortage of informative training data and the high computational costs of high-dimensional problems. Specifically, the...
This Project Grant award of $630,763 from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports research at the Georgia Tech Research Corporation to develop a systematic machine learning framework for modeling multiscale fluid dynamics phenomena. The objective is to create explicit mathematical models that can accurately predict multiscale processes underlying applications such as weather forecasting, climate modeling, fusion energy, and other scientific and...
This $499,624 National Science Foundation project grant supports research at the University of Pittsburgh to develop physics-guided machine learning methods for turbulent flow simulation. Funded under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), the three-year award aims to advance computational fluid dynamics capabilities. Specifically, the university researchers will create a new deep learning model incorporating physical constraints to reconstruct...
This Project Grant award of $474,874 from the National Science Foundation's Engineering program (CFDA 47.041) supports collaborative research by The Pennsylvania State University (Penn State) to enable engineers to use small-scale models in lower-cost wind tunnels while capturing the full-scale behavior of rotating wakes. The research aims to validate the hypothesis that rotor thrust and induced power coefficient can sufficiently replicate vortex wake turbulence and stability across scales....
This Project Grant from the National Science Foundation's Division of Chemical, Bioengineering, Environmental, and Transport Systems will fund $900,000 over three years to develop new physics-informed deep learning methods for discovering probabilistic turbulence closure models. The University of Pittsburgh will utilize machine learning algorithms and high-performance computing to solve the forward and inverse probability density function transport equations governing turbulent flows. If...
This Project Grant from the National Science Foundation Division of Mathematical Sciences provides $748,840 to The Pennsylvania State University under the Mathematical and Physical Sciences program (CFDA 47.049) from September 1, 2022 to August 31, 2025. The funding supports research into partial differential equations modeling incompressible fluids and elastic solids, with a focus on fluid-structure interaction problems, transport of vectors by fluid flow, and seismic fault monitoring. The...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) provides $399,998 to the University of Texas at Austin to develop novel algorithms that integrate classical numerical schemes and deep learning techniques to address complex scientific computing challenges. The key objectives are to leverage the flexibility of neural networks, the stability and convergence properties of numerical methods, and the computational power of...
This Project Grant from the National Science Foundation Division of Mathematical Sciences provides $429,158 to develop computational tools for modeling, prediction and control of distributed and reconfigurable renewable energy systems. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), key outcomes include noise-resilient identification methods for transient dynamics, stochastic models integrating statistical closure with topology-aware data, and optimal control...
The National Science Foundation (NSF) Directorate for Engineering awarded a 5-year, $626,006 CAREER grant to The Pennsylvania State University, doing business as Penn State, to support research that enables dynamic modeling, control, and design for next-generation autonomous aerial systems with enhanced capabilities and operational efficiency. The project aims to establish a foundational framework that could revolutionize the entire lifecycle of autonomous systems, from production to...
The Trustees of the University of Pennsylvania received a three-year $426,556 project grant from the National Science Foundation to support research titled "COLLABORATIVE RESEARCH: LEVERAGING FLUID-STRUCTURE INTERACTIONS FOR EFFICIENT CONTROL IN GEOPHYSICAL FLOWS" under the NSF Engineering program (CFDA 47.041). The University will conduct collaborative research from November 2021 to October 2024 leveraging fluid-structure interactions to develop more efficient control technologies for...