Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach

Abstract

Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.

References in corpus (5)

• pymoo: Multi-objective Optimization in Python
• Sequential design of computer experiments for the estimation of a probability of failure
• Bayesian subset simulation
• Yield Optimization based on Adaptive Newton-Monte Carlo and Polynomial Surrogates
• A Blackbox Yield Estimation Workflow with Gaussian Process Regression Applied to the Design of Electromagnetic Devices

Cited by in corpus (1)

• Multi-Objective Yield Optimization for Electrical Machines using Machine Learning