YIELD OPTIMIZATION BASED ON ADAPTIVE NEWTON-MONTE CARLO AND POLYNOMIAL SURROGATES

DOI10.1615/Int.J.UncertaintyQuantification.2020033344zbMath1498.62155arXiv1912.09908OpenAlexW3045526184MaRDI QIDQ5052403

Ulrich Römer, Sebastian Schöps, Mona Fuhrländer, Niklas Georg

Publication date: 24 November 2022

Published in: International Journal for Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.09908

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Design of statistical experiments (62K99) Monte Carlo methods applied to problems in optics and electromagnetic theory (78M31)

Related Items (3)

Hermite least squares optimization: a modification of BOBYQA for optimization with limited derivative information ⋮ Efficient Yield Optimization with Limited Gradient Information ⋮ A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices

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