TY - JOUR
T1 - Detecting and adapting to parameter changes for reduced models of dynamic data-driven application systems
AU - Peherstorfer, Benjamin
AU - Willcox, Karen
N1 - Funding Information:
This work was supported in part by AFOSR grant FA9550-11-1-0339 under the Dynamic Data-Driven Application Systems (DDDAS) Program, Program Manager Dr. Frederica Darema.
Publisher Copyright:
© The Authors. Published by Elsevier B.V.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Dynamic data-driven application systems
KW - Dynamic reduced models
KW - Model reduction
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U2 - 10.1016/j.procs.2015.05.363
DO - 10.1016/j.procs.2015.05.363
M3 - Conference article
AN - SCOPUS:84939172541
VL - 51
SP - 2553
EP - 2562
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
IS - 1
T2 - International Conference on Computational Science, ICCS 2002
Y2 - 21 April 2002 through 24 April 2002
ER -