Excitation and System Parameter Identification of Dynamic Systems by an Inverse Meta-Model  
Author Gordon J. Savage


Co-Author(s) Young Kap Son


Abstract In the inverse problem, either the corresponding component parameters or the corresponding input signal is obtained for a given output or response. Most model-based solutions to the inverse problem involve optimization using the so-called forward model. The forward model typically comprises the mechanistic model in some form. Most commonly, inverse problems are formulated in a static setting where a wealth of theoretical results and numerical methods are available. However, there are many important dynamic applications wherein time-dependent information needs to be discerned from time-dependent data. Recently, data-based approaches, or model-free methods, have been invoked whereby feature extraction methods such as Support vector machines (SVM) and artificial neural networks (ANN) are used. Herein we develop an inverse solution for dynamic systems through least-squares meta-model mathematics. Single-value decomposition (SVD) makes any matrix inversion tractable. The inverse meta-model is compared to the optimization method, using mechanistic models for fidelity, and is shown to have similar accuracy but much increased speed.


Keywords Inverse problem, Dynamic system, Inverse meta-model
    Article #:  RQD27-13

Proceedings of 27th ISSAT International Conference on Reliability & Quality in Design
Virtual Event

August 4-6, 2022