Project Grant 2208504
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $399,998 to the University of Texas at Austin (UT Austin) to develop innovative numerical algorithms that integrate classical numerical schemes and deep learning techniques. The goal is to address complex scientific computing challenges, such as simulating high-dimensional, fully nonlinear differential equations, long-term Hamiltonian system simulations, and...
- This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to Texas Tech University System provides $249,394 to develop novel machine learning (ML) models that can accurately approximate kinetic equations used to model non-equilibrium phenomena in physics and engineering. The project aims to create reduced-order ML moment models that can capture the underlying physics while preserving key mathematical properties like hyperbolicity,...
- This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences (CFDA 47.049) program provides $140,889 to Texas A&M University to conduct research connecting machine learning and numerical methods for partial differential equations. The key objectives are to leverage deep learning techniques to improve numerical methods for PDEs, and apply the theoretical understanding of finite element methods to better comprehend the success of deep neural networks....
- This National Science Foundation Project Grant of $229,021 awarded on August 1, 2022 will support research at the University of Texas at Austin to develop mathematical frameworks in optimal transport applications to probability, machine learning, and kinetic theory through July 31, 2025. Under the Mathematical and Physical Sciences program (CFDA 47.049), the investigator will advance understanding of stochastic modeling, artificial intelligence algorithms, and kinetic theory by exploiting...
- The National Science Foundation Division of Mathematical Sciences awarded a $379,925 project grant to the University of Texas at Austin for collaborative research on new challenges in the derivation and dynamics of quantum systems. This funding supports research activities from July 1, 2021 to June 30, 2024 under the Mathematical and Physical Sciences program (CFDA 47.049). The goal of the Mathematical and Physical Sciences program is to promote progress in mathematics and physics and strengthen...
- 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...
- This Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) provides $199,998.00 to conduct research on fundamental kinetic theory models in physics. The objective is to gain a comprehensive mathematical understanding of several complex kinetic models, with a focus on the quantum Landau equation and the existence of regular solutions to the inhomogeneous Landau equation. The research aims to open new research directions within...
- This National Science Foundation Project Grant of $249,068 supports computational and theoretical research on the interaction between spin-lattice dynamics in paramagnetic iron at high pressures and temperatures. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the research team at the University of Texas at El Paso will develop machine learning models and evolutionary computation techniques to efficiently simulate phonon dispersion relations while accounting for spin...
- This Project Grant from the National Science Foundation Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $382,648 to the University of Texas at Austin for research investigating partial differential equations methods in the study of interfaces from July 1, 2022 to June 30, 2025. The award supports research training for graduate students and postdoctoral researchers in two thematic areas: developing mesoscale and macroscopic models...
- The National Science Foundation (NSF) awarded a $300,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Texas at Austin (UT Austin). The grant, with a period of performance from July 15, 2025 to June 30, 2028, will support UT Austin's research on "Large Systems of Interacting Particles and Waves and their Effective Equations." The project aims to advance mathematical models that connect large quantum systems of interacting...
This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program totaling $449,995 supported research at the University of Texas at Austin from July 15, 2022 to June 30, 2025. The research aims to develop machine learning and parallel-in-time algorithms to efficiently simulate multiscale dynamical systems, reducing overall computation time for applications in physical science and engineering. Specifically, the researchers will construct effective solution operators for multiscale systems by combining modern machine learning approaches with properly sampled training data that captures the physics and causality. A self-improving iterative procedure is proposed to enable massive parallel-in-time computation while improving machine learning accuracy. The funding will directly involve two graduate students and support undergraduate mentoring. This award addresses the program's goal of strengthening the nation's scientific enterprise through advancing mathematical and physical sciences knowledge.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $450.0k | 7/13/22 |