This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program ($300,909) supports collaborative research between Mississippi State University and Auburn University from June 2023 through May 2026. The award enables development of a novel optical trapping technique to precisely control and diagnose interactions between dust particles and plasma. Researchers will design an optical system to pin down and transport single dust grains in plasma, and employ...
This $357,868 National Science Foundation project grant supports collaborative research between Mississippi State University and Auburn University to develop a novel method for trapping and controlling dust particles in low-temperature plasmas. The grant funds development of an optical trapping technology to pin down and transport single dust particles anywhere in a plasma device. Multiple laser diagnostic techniques will also be used to measure impacted plasma parameters and aid understanding...
This $457,976 Project Grant awarded by the National Science Foundation's (NSF) Division of Astronomical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports research to develop a novel framework for constraining models of baryonic feedback in the universe. The research team at the University of Chicago will apply artificial intelligence and machine learning techniques to data from cosmic microwave background and galaxy surveys to gain insights into the...
This $285,606 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports a computational study of energy distribution and dissipation in weakly collisional plasmas. The project will develop reduced plasma physics models and integrate them into an open-source simulation code applicable to astrophysics, space weather, and fusion energy research. Key project activities include leveraging novel machine...
This $450,000 project grant from the National Science Foundation (NSF) Division of Physics will fund the development of machine learning-augmented density functional theory (DFT) tools to enable faster and more accurate quantum simulations of warm dense plasmas. Under the Mathematical and Physical Sciences program (CFDA 47.049), the University of Rochester will receive funding from August 15, 2022 to July 31, 2025. The project aims to advance finite-temperature exchange-correlation functionals...
This $350,000 federal Project Grant was awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program. The goal of the research is to develop accurate mathematical models and computer simulations for studying non-equilibrium systems with memory effects, such as those found in biosystems, plasma evolution, and solid-state nanostructures. The Principal Investigator will focus on analytical and numerical approaches to statistical transport...
The National Science Foundation awarded Columbia University a $419,433 project grant under the Mathematical and Physical Sciences program (CFDA 47.049). The grant supports research to advance data-driven modeling approaches for magnetized plasmas through three key efforts. First, several emerging data decomposition methods will be applied to numerical simulations of magnetized plasmas for the first time and assessed for these systems. Second, data-driven nonlinear models based on these...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) provides $360,405 to the University of New Mexico to develop AI/ML-based computational tools that enable novel searches for the physical origin and properties of the invisible elements that make up the cosmos. The project aims to determine whether neutrinos have nonstandard interactions that drive cosmic expansion and the growth of large-scale structure. It...
This $539,134 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) will support the development of a novel artificial intelligence (AI) and machine learning (ML) methodology to automatically discover the fundamental partial differential equations (PDEs) governing the Earth's radiation belt dynamics. The overarching goal is to enable significant breakthroughs in geoscience research by identifying the driving physical processes behind the dynamic evolution...
This Project Grant, awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports collaborative research on microscale plasma processes in shock and turbulent environments. The $341,464 award to Columbia University, spanning August 2024 to July 2027, will focus on two key areas: 1) particle heating and acceleration in shocks, and 2) thermodynamics and transport driven by large-scale turbulence. The research aims to develop...
This $728,539 federal Project Grant award to Emory University, funded by the National Science Foundation's (NSF) Division of Physics, supports research to "bridge the gap between particle-based and continuum hydrodynamic descriptions of dusty plasma" using artificial intelligence (AI) and machine learning (ML) techniques. The project aims to derive new physical and statistical laws governing the complex motion of dust particles in plasma, which are common throughout the universe. The research leverages advanced 3D particle tracking data to train ML models that can uncover underlying physical principles behind the observed particle dynamics, even in regimes where traditional approximations break down. In addition to the core research, the award facilitates various educational and public outreach activities, such as an after-school science club for elementary school students. This award reflects NSF's mission to advance scientific knowledge and promote interdisciplinary collaboration between physics, computer science, and applied mathematics.