Constitutive model calibration and uncertainty quantification using full-field data

The calibration of constitutive models is essential for reliable simulation results and has re-gained significant research interest due to the development of full-field measurement techniques, such as digital image correlation or infrared thermography. Full-field measurement methods allow to measure vast amounts of experimental data, typically on the surface of specimens or components. If experiments are performed appropriately an information-rich field of data in space and time is obtained for model calibration. The deterministic model calibration is typically done using the nonlinear least-squares method and was recently extended by quantification of parameter uncertainties.  The quantification of uncertainties comprises even considering the propagation of uncertainties during step-wise model calibration schemes that inevitably renders simulation results uncertain as well. To propagate uncertainties even to simulation results, the first-order second-moment method is chosen as flexible and efficient approach.