Collaborative Research: Calibrating Digital Twins in the Era of Big Data with Stochastic Optimization

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This project will contribute to the national prosperity by providing new calibration methods to generate value-producing opportunities for digital twins in many applications, including energy, healthcare, and manufacturing. A digital twin is a digital representation of a complex physical system that can be useful for monitoring, forecasting, and testing the system in a virtual world. Parameter calibration of digital twins with observational data is one of the most important steps in enabling them to closely replicate a physical system. Today, advanced data sensing and collection technologies provide massive data points from many components of a complex system. The success of this project will provide a means of robust estimation by efficient sampling from these large datasets, thereby significantly reducing the computational burden of calibration. The outreach activities of the project will improve workforce preparation through engagement with industrial practitioners, broaden participation through involvement of underrepresented students in research, and provide opportunities for K-12 students to learn about the field of data science.Quantitative methods established during this project for digital twin calibration will fully leverage the power of Big Data while addressing the research challenges brought forth by the size and complexity of the datasets. Specific research tasks include: development of stochastic optimization approaches reconciled with statistical theories that will optimally guide simulation experiments by identifying the best (smallest most informative) subsets of data for computational efficiency; extending the integrative optimization framework to be applicable for a wide range of calibration problems, including multi-dimensional, functional, and time-variant calibrations, with theoretical and practical implications; and seamless incorporation of input uncertainty with optimization to dramatically enhance the solution's robustness while maintaining computational tractability. The approach will be validated through real-word case studies in building energy systems and wind power systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.