Parameter estimation assumes that the model is an accurate representation of the system being studied and that any deviations are caused by measurement noise. For real experimental data this is often not the case. Clearly, the model will constructed to the highest fidelity by the analyst but when it is deficient, the remedy is not always obvious. One approach is to include a discrepancy function which one hopes will resolve any differences. The paper describes the use of such a function combined with Kalman filtering and meshless FEA.

This content is only available via PDF.
You do not currently have access to this content.