Enhanced Insights from Vehicle Simulation by Analysis of Parametric Uncertainties

Beschreibung

There is a great history of embedding computer-aided technologies in engineering workflows. However, we are only on the brink of tapping into the potential of digitalization. With the rise of advanced analytics and machine learning, in ever shorter development cycles, we will be able to gain more insight, handle complexity, and design better products. The motivation of this thesis is to explore, compare, and promote methods and workflows to aid the understanding and design of complex systems.

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Simon Schmeiler

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Sensitivity Estimation Machine Learning Vehicle Simulation

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