Detecting and adapting to parameter changes for reduced models of dynamic data-driven application systems

Benjamin Peherstorfer, Karen Willcox

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2553-2562
Number of pages10
JournalProcedia Computer Science
Issue number1
StatePublished - 2015
EventInternational Conference on Computational Science, ICCS 2002 - Amsterdam, Netherlands
Duration: Apr 21 2002Apr 24 2002


  • Dynamic data-driven application systems
  • Dynamic reduced models
  • Model reduction

ASJC Scopus subject areas

  • General Computer Science


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