Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems
Name
Willcox_Detecting and adapting.pdf
Size
1.9 MB
Format
Adobe PDF
Checksum (MD5)
7af469587e1173b5d2bc473f2e4782b0
Author(s)
Peherstorfer, Benjamin
Willcox, Karen E
Date Issued
June 2015
Journal
Procedia Computer Science
Publisher
Elsevier
Citation
Peherstorfer, Benjamin, and Karen Willcox. “Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-Driven Application Systems.” Procedia Computer Science 51 (2015): 2553–2562.
Version
Final published version
Abstract
We consider the task of dynamic capability estimation for an unmanned aerial vehicle, which is needed to provide the vehicle with the ability to dynamically and autonomously sense, plan, and act in real time. Our dynamic data-driven application systems framework employs reduced models to achieve rapid evaluation runtimes. Our reduced models must also adapt to underlying dynamic system changes, such as changes due to structural damage or degradation of the system. Our dynamic reduced models take into account changes in the underlying system by directly learning from the data provided by sensors, without requiring access to the original high-fidelity model. We present here an adaptivity indicator that detects a change in the underlying system and so allows the initiation of the dynamic reduced modeling adaptation if necessary. The adaptivity indicator monitors the error of the dynamic reduced model by comparing model predictions with sensor data, and signals a change if the error exceeds a given threshold. The indicator is demonstrated on a deflection model of a damaged plate in bending. Local damage of the plate is modeled by a change in the thickness of the plate. The numerical results show that in this example the adaptivity indicator detects all changes in the thickness and correctly initiates the adaptation of the reduced model.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.procs.2015.05.363
