Article2016

Multidimensional Uncertainty Quantification of Microwave/RF Networks Using Linear Regression and Optimal Design of Experiments

Aditi Krishna Prasad · Majid Ahadi · Sourajeet Roy

43 citations0/0 files storedIEEE Transactions on Microwave Theory and Techniques

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Probabilistic and Robust Engineering Design Optimal Experimental Design Methods Advanced Multi-Objective Optimization Algorithms

Abstract

In this paper, a nonintrusive polynomial chaos (PC) approach for the fast multidimensional uncertainty quantification of microwave/RF networks is presented. The key feature of this paper is the development of a linear regression methodology based on sparse D-optimal design of experiments to accurately evaluate the PC coefficients of the network responses. A greedy search algorithm to identify the sparse set of D-optimal design of experiments from within a multidimensional random space has been presented. Additional numerical approaches to further accelerate the search algorithm for high-dimensional random spaces have also been developed.

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References

2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations 1972Theory of optimal experiments 1972Theory of Optimal Experiments.2010Numerical Methods for Stochastic Computations: A Spectral Method Approach 1980A Comparison of Algorithms for Constructing Exact D-Optimal Designs 2009Recent Advances in Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Analysis and Design 2013Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos 1992A review of some exchange algorithms for constructing discrete D-optimal designs

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