Mathematical models are ubiquitous in applications across science and technology, where these models are used to describe complex processes. To systematically investigate and predict phenomena of interest using computer simulations based on these models, we need accurate, reliable, and efficient computational methods.

Many complex models of practical relevance are affected by uncertainties, for example due to a lack of knowledge of material properties, intrinsic natural variability, or by incorporating noisy data. Such uncertainties inevitably impact the considered mathematical model and consequently the quantification of the reliability of simulation outcomes.

The junior research group Uncertainty Quantification (UQ) develops advanced mathematical and numerical techniques for the treatment and quantification of uncertainties at the interface of complex computational models and data. To this end, we work on methods of modern applied mathematics, machine learning techniques in conjunction with simulation-based approaches, and aspects of high-performance computing. Overall, our research focuses on theoretical and methodological aspects, as well as on interdisciplinary projects where theoretical sound methodologies are tailored to applications.