October 2024
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4 Reads
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October 2024
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4 Reads
August 2024
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28 Reads
IEEE Transactions on Components, Packaging, and Manufacturing Technology
This paper investigates the application of tensor decomposition and the stochastic Galerkin method for the uncertainty quantification of complex systems characterized by high parameter dimensionality. By employing these methods, we construct surrogate models aimed at efficiently predicting system output uncertainty. The effectiveness of our approaches is demonstrated through a comparative analysis of accuracy and CPU cost with conventional Galerkin methods, using two transmission line circuit examples with up to 25 parameters.
April 2024
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22 Reads
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3 Citations
IEEE Transactions on Circuits and Systems I Regular Papers
Recently, Polynomial Chaos (PC) has been proposed as efficient approach for performing uncertainty quantification in the the context of electronic design automation. A related approach, Rational Polynomial Chaos (RPC) was later developed for applications that exhibit a large variation in the quantity of interest. One of the key bottlenecks for both PC and RPC is the number of circuit evaluation needed, particularly as the number of random parameters increases (often referred to as the curse of dimentionality). In this paper, we propose an approach that uses sensitivity information to significantly reduce the number of circuit evaluations needed for Rational Polynomial Chaos. Numerical examples are given to illustrate the accuracy and efficiency of the proposed approach.
January 2024
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4 Reads
IEEE Transactions on Components, Packaging, and Manufacturing Technology
In [1], we proposed the methodology to use the sensitivity information for building the Least-Squares SVM-based surrogate model for uncertainty quantification in the context of circuit systems. It was shown in [1] that the sensitivity-enhanced LS-SVM could successfully reduce the simulation data required for building LS-SVM-based surrogate models. However, the number of samples required for building the surrogate model is not known a priori. In this paper, we present an iterative technique that uses the nested Latin hypercubes to add the samples until the surrogate model achieves the desired accuracy. The presented technique is demonstrated using two numerical examples, where we show that the proposed method, when used with [1], can significantly reduce the amount of simulation data required for building LS-SVM-based surrogate models.
October 2023
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3 Reads
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1 Citation
... The proposed PEEC-MOR method is validated through numerical results in both the frequency domain and time domain. Data-driven derivative-enhanced modeling techniques have proven to be successful in building accurate models with a reduced computational effort [16], [17], [18]. ...
April 2024
IEEE Transactions on Circuits and Systems I Regular Papers