Project Grant 2436231

Award Date 3/1/25
Completion Date 2/29/28
Dollars Obligated $270K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Pojoaque, NM 87501, USA
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This Project Grant award, funded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports fundamental research to improve the generalization capabilities of digital twin models for complex systems. The $269,187 grant will enable researchers at Smith College to develop hybrid digital twin architectures that combine physics-based and domain-agnostic components, allowing for improved predictive performance across a range of conditions,...
The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Texas at Austin. The grant, titled "COLLABORATIVE RESEARCH: MATH-DT CLOSING THE GENERALIZATION GAP OF DIGITAL TWINS," aims to develop fundamental theories and robust digital twins that can accurately predict outcomes for complex systems under extreme or unexpected conditions. The project will focus initially on modeling human...
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This Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $269,979 to the Santa Fe Institute of Science to conduct collaborative research on developing robust digital twin models that can accurately predict the behavior of complex systems under unexpected conditions. The key objectives are to:

  1. Investigate the generalization abilities of digital twins by combining mathematical tools from nonlinear dynamics and machine learning techniques. This research will focus on applying the digital twin framework to model human circadian rhythms and explore interventions to address disruptions like jet lag.

  2. Create a new class of hybrid digital twins that leverage both physics-based and domain-agnostic components, allowing practitioners to balance expressivity and generalization as needed. The project will quantify how adjusting the weights of these components impacts the digital twin's out-of-distribution performance.

  3. Provide research and educational opportunities for undergraduate and graduate students to gain hands-on experience in mathematical modeling, data analysis, and building advanced machine learning pipelines.

The award period is from March 1, 2025 to February 29, 2028, and no sub-awards are planned under this grant.

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