Dear colleagues, We are looking for a PhD student for the following project: For the design of safety critical components and structures, it is vital to ensure that integrity and reliability are maintained under all environmental conditions and feasible parameter settings. Physics based simulation models such as the Finite Element Method provide means to explore the behavior of a structure for many more scenarios than what can be measured experimentally. The development of an appropriate model either on the structural level or for the constitutive behavior of the materials involved is a complex task. The modelling assumptions can only be validated based on experimental data, which are either provided by databases with lab tests under different conditions on the material scale, or using monitoring techniques for a structural modelling problem. The challenge is that simulation models usually have multiple parameters that jointly influence the simulation results , i.e. there is not a one to one mapping between a single parameter and a single data set. As a consequence, an inverse problem has to be solved. The goal of this proposal is to 1. automate the procedure of calibrating model parameters based on experimental data including objective measures of the model quality 2. identify model discrepancies - which is particularly difficult for large datasets - by combining using Gaussian process models with physics based models 3. and suggest model improvements based on a dictionary type of learning procedure. All of the procedures are implemented in a Bayesian framework to account for measurement and model uncertainties. The validation of the developed methods is initially performed using virtual experiments allowing to directly prescribe model deficiencies and compare the identified models to the ground truth. The implementation of the models is done in an open source framework. As a consequence, the procedures are planned to be validated in parallel projects on real data sets. Details: https://www.bam.de/umantis/EN/952.html Kind regards, Philipp Benner