Project Grant 2315963

Award Date 9/1/23
Completion Date 8/31/26
Dollars Obligated $344K
Federal Grant Program
47.041
Assistance Type
Project Grant
Place of Performance
Madison, WI 53715, USA
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The National Science Foundation (NSF) Directorate for Engineering (CFDA Program 47.041) awarded a $344,126 Project Grant to the University of Wisconsin System for a 3-year research project focused on developing new mathematical methods and computational tools to enable the use of complex data formats, such as visual and thermal images, for advanced model predictive control (MPC) systems. The key objectives are to:

  1. Integrate concepts from control theory, topology, machine learning, and Bayesian analysis to create a scalable paradigm for MPC that can effectively leverage complex data sources beyond single-point measurements.

  2. Develop fast and scalable uncertainty quantification strategies to study the tradeoffs between computational tractability and control performance in large-scale machine learning and physics-based models.

  3. Demonstrate the effectiveness of the new MPC formulation through applications in energy, manufacturing, and materials systems.

The award also supports the development of new educational materials and computational tools to help students across K-12, undergraduate, and graduate levels better visualize and make sense of complex data, which are essential skills for data-driven science and engineering careers.

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