Reliability and Probabilistics

Probabilistic methods improve knowledge acquisition from experiments and model predictions by an explicit dealing with uncertainties. We address the following fields:
Parameter estimation of correlated physical quantities with methods of Bayesian statistics
Reliability prediction of ceramics using micro-structurally-based fracture mechanics approaches

Bayesian statistics: Pushing the resolution limits for sensitive experiments

Materials characterization: Identify mechanisms for deformation mechanisms using probabilistic model selection approaches

Reliability: Weakest link approaches, probabilistic fracture mechanics, lifetime prediction

Data analysis : exploratory analysis, parameter uncertainties, scaling and extrapolation models (e.g. for accelerated testing), pooling

Probabilistic tools for: image analysis, neural networks, stochastic geometry, random processes


Current Projects and Cooperations:
  • HGF School IMD (Integrated Materials Development for High Temperature Alloys) (2013-2020) (IAM, ITM)