Uncertainty Quantification with Surrogate Models for Plastics Flow in Manufacturing Engineering

Invitation to the presentation as pdf

With the help of simulation-based tools, we wish to make reliable assertions and predictions
for quantities of interests (QoI), also in the presence of uncertainty. Thus, methods from
Uncertainty Quantification (UQ) can enhance the quality of processes and products through
quantified probability measures. We consider sampling-based UQ methods that usually
require a great number of model evaluations. Here, using surrogate models, which are
computationally cheaper, may be necessary. Therefore, we first explore the benefits of
intrusive Model Order Reduction (MOR) techniques. As an alternative, we also investigate the
advantages of Gaussian Process Regression (GPR) as a meta-model. Finally, the integration of
the resulting surrogate models into a UQ setting is demonstrated for applications coming
from polymer processing.

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Dr.-Ing. Fabian Key

Institute of Lightweight Design and Structural Biomechanics, TU Wien