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Uncertainty quantification and optimization under uncertainty become increasingly important in the context of electromagnetic field simulation since devices are developed closer to their physical limits. Deviations in the manufacturing process of electronic components may lead to rejections due to malfunctioning. Uncertain design variables can be modeled as random variables. Then, the probability that a product fulfills its performance specifications is called the yield. The performance specifications are restrictions involving the partial differential equations describing the electromagnetic field, i.e., Maxwell’s equations in time or frequency domain. The aim of this research is to estimate the yield by finding an efficient approximation and then maximizing the yield, which corresponds to minimizing the failure probability. Thereby the quality of the electrical device is improved and time and resources can be saved.

Monday 10:00-11:00 and by appointment (in either case, please contact me by email beforehand)

• 2020: Young scientist award at URSI KHB 2020 conference for the contribution 'Yield Optimization using Hybrid Gaussian Process Regression and a genetic Multi-Objective Approach'

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