This Project Grant award of $134,150 from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports research by the University of Pennsylvania to advance the design, analysis, and deployment of rigorously justified nonconvex optimization algorithms. The project aims to create guaranteed procedures for training practical machine learning systems with improved reliability, robustness, and efficiency. Specifically, the research will focus on...
This $477,585 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research to develop new methods, algorithms, and software that integrate machine learning/artificial intelligence (ML/AI) with traditional physical knowledge in "physics-informed machine learning" (PIML) models. The project aims to create a cyberinfrastructure that enables the seamless and synergistic integration of ML/AI with...
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 award of $325,000.00 was provided by the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The award supports collaborative research by a team at Emory University to develop acceleration and preconditioning methods for improving the training of large artificial intelligence (AI) models. The research aims to leverage numerical analysis and linear algebra techniques to...
The National Science Foundation (NSF) awarded a $285,669 Project Grant to Louisiana State University (LSU) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049). The grant, awarded on July 15, 2025, will fund a research project focused on establishing a unified framework for adaptive sampling to enhance scientific machine learning algorithms. The project aims to optimize the selection of random samples in the training set for solving high-dimensional partial...
This Project Grant award of $200,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research to advance artificial intelligence (AI) and optimization through the study of partial differential equations (PDEs) on Wasserstein space. The award recipient, the Regents of the University of Michigan, will conduct theoretical analysis of master equations and particle systems to address key challenges in AI, including derivative-free...
This $148,654 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program aims to develop statistical tools to improve the reliability of artificial intelligence (AI) systems used in real-world applications like automated decision-making, financial forecasting, and neuroscience research. The research will focus on establishing mathematically rigorous methods for uncertainty quantification to build trustworthy AI, with applications in enhancing...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program, with CFDA number 47.049, will fund research to address the challenge of reliable and interpretable reinforcement learning (RL) systems in complex, data-limited environments. The $154,999 award, effective August 1, 2025 through July 31, 2028, aims to develop theoretical foundations and methods for robust inference and decision-making in RL, including tools for contextual bandits...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $107,860 Project Grant to the Regents of the University of Minnesota, Office of Sponsored Projects Administration, a non-profit 1862 land grant college, to conduct research under the NSF Mathematical and Physical Sciences program (CFDA 47.049). The research project will develop theoretical foundations for using machine learning methods to solve high-dimensional partial differential equations, emphasizing predictive...
The National Science Foundation (NSF) awarded Arizona State University a 5-year, $196,726 CAREER Program grant under the Computer and Information Science and Engineering (CISE) grant program (CFDA 47.070) to support a research and education program focused on accelerating scientific discovery through physics-informed deep learning models. The project aims to develop physically-consistent dynamics models, deep learning-based symbolic regression algorithms, and multimodal deep learning...