Project Grant DESC0025508

This three-year, $200,000 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program will fund the development of more computationally efficient methods for solving high-dimensional problems that commonly arise in scientific, engineering, and commercial computing. Specifically, the grant recipient, Stanford University, will improve randomized quasi-Monte Carlo sampling techniques and develop a median-of-means strategy to better handle problems involving large...

This $170,000 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences supports research on developing randomized algorithms for solving inverse problems and quantifying uncertainty in hierarchical Bayesian and dynamical inverse problems. Key goals include: (i) creating efficient algorithms to estimate uncertainty in the hyperparameters that govern Bayesian inverse problems, and (ii) developing new iterative methods leveraging randomization to...

Radiasoft LLC was awarded a $202,034 Project Grant from the Department of Energy Office of Science on June 27, 2022 to develop conformal mapping techniques for high-speed simulations of structure wakefield accelerators. The grant is part of the Office of Science Financial Assistance Program (CFDA 81.049), which supports basic research to deliver scientific discoveries and tools that transform understanding of nature and advance U.S. energy security and economic competitiveness. Under the...

This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) provides $220,000 to Rensselaer Polytechnic Institute (RPI) from August 1, 2025 to July 31, 2028. The award will fund the development of new fast algorithms for solving Helmholtz problems and associated eigenvalue problems. These efficient computational methods will have applications in areas such as the design and optimization of engineering devices,...

This $170,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at North Carolina State University (NC State) to develop new randomized algorithms for solving large-scale inverse problems and quantifying uncertainty in hierarchical Bayesian models. The key research objectives are: (i) to create efficient algorithms for quantifying uncertainty in the hyperparameters governing Bayesian inverse...

This four-year Project Grant from the National Science Foundation's Division of Information and Intelligent Systems and Computer and Information Science and Engineering program will provide $1,199,743 to develop grid-free Monte Carlo methods for digital geometry processing problems. Carnegie Mellon University will receive funding to expand the set of partial differential equations that can be solved using scalable, reliable Monte Carlo techniques without traditional discretization. The...

This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $450,000 to Colorado State University (CSU) from August 1, 2024 to July 31, 2027. The project aims to develop a novel adaptive, fully anisotropic multiscale hp-element method to revolutionize simulation-based design in computational electromagnetics (CEM). The research will create exponentially convergent techniques to accurately and efficiently model multiscale, non-smooth behavior...

This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will provide $350,000 to North Carolina State University (NC State) from July 1, 2025 to June 30, 2028. The project will investigate foundational principles for mathematically representing and numerically solving large-scale random models that arise in areas such as data analysis, AI, geophysics, signal processing, and medical imaging. New strategies and methodologies...

This Project Grant from the National Science Foundation Division of Mathematical Sciences provides $149,999 to support the development of new algorithms for ensemble data assimilation in large-scale applications that do not rely on Gaussian approximations. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the award will be carried out from August 1, 2022 to July 31, 2025 by researchers at the University of Colorado Boulder. The project aims to advance data assimilation...

This Project Grant award of $220,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) to Rensselaer Polytechnic Institute (RPI) focuses on developing new stochastic algorithms for solving minimax-structured nonconvex nonsmooth optimization problems with applications in machine learning. The project aims to create new optimization algorithms that can deliver stable and reliable solutions for training robust deep learning models, which are often...