The National Science Foundation awarded a $268,851 Project Grant to the University of Houston System through the Mathematical and Physical Sciences program (CFDA 47.049). The grant will fund research into developing tensorial reduced order models for numerical simulation of dynamical systems from July 2023 through June 2026. Specifically, the University of Houston will work to advance parametric model reduction techniques using tools from multi-linear algebra. This includes developing a...
The University of Houston will use a $236,370 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to study how nonlocal diffusion shapes patterns in biological systems from June 1, 2023 to May 31, 2026. Specifically, the university will analyze how nonlocal seed dispersal versus local diffusion impacts pattern formation in arid ecosystems exhibiting banded vegetation, using a nonlocal Gray-Scott model. Researchers will develop methods to...
The University of Houston was awarded a three-year, $479,959 Project Grant from the National Science Foundation Division of Chemistry under the Mathematical and Physical Sciences program (CFDA 47.049). The grant funds research titled "Interfacial Control Through Adsorbate Design Offers Fundamental Insights and Practical Utility" to be conducted in Houston, Texas from November 2021 through October 2024. The grant supports the University's efforts to advance understanding of major...
The University of Houston was awarded a $215,000 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to support the project "QUANTUM CORRELATIONS IN INFORMATION THEORY, THEIR INTERPLAY, AND THEIR LIMITATIONS" from July 1, 2021 to June 30, 2024. Under this award, the University of Houston will investigate quantum correlations in information theory, explore the interplay between different types of quantum correlations, and examine...
The University of Houston received a three-year Project Grant award of $752,202 from the National Science Foundation to develop stochastic models for nonlinear and many-body dynamics in molecular semiconductors. Funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), this award will support research into increasing understanding of major problems in the progress of mathematical and physical sciences. Specifically, the University of Houston will utilize the funding to...
The National Science Foundation Division of Mathematical Sciences awarded a $149,783 Project Grant to Texas A & M University from July 2021 through June 2024 under the Mathematical and Physical Sciences program (CFDA 47.049). The grant funds research titled "CDS & E-MSS: OPTIMAL RECOVERY IN THE AGE OF DATA SCIENCE" to promote progress in the mathematical and physical sciences. Specifically, the university will leverage data science techniques to advance understanding of major...
The National Science Foundation awarded a $225,000 Project Grant to Texas A&M University under the Mathematical and Physical Sciences program (CFDA 47.049) for the period of November 1, 2021 through October 31, 2024. The grant funds collaborative research on new perspectives for deep learning by bridging approximation, statistical, and algorithmic theories. The Mathematical and Physical Sciences program aims to advance scientific knowledge and understanding in core areas of mathematics and...
The National Science Foundation awarded a $249,460 project grant to Texas A&M University from July 1, 2021 to June 30, 2024 under the Mathematical and Physical Sciences program (CFDA 47.049). The university will research the geometry of graphs and Banach spaces over this three-year period. The Mathematical and Physical Sciences program aims to advance these fields and strengthen the nation's scientific enterprise through increased knowledge and understanding of major problems. This...
This $229,461 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports the development and analysis of novel self-supervised probabilistic graph structure learning models. The goal is to uncover latent representations hidden within large datasets, which can provide valuable insights across diverse applications like cancer research and environmental analysis. The research will involve creating advanced mathematical models,...
This $305,850 federal Project Grant award was provided by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The project aims to advance the analysis and comparison of relaxed shape matching problems, investigate supervised and unsupervised deep learning approaches to improve the efficiency of manifold registration algorithms, and study extensions to account for partial or incomplete data and...