STOCHASTIC COLLOCATION FOR THE STATISTICAL ASSESSMENT OF RLC CIRCUIT: A COMPARATIVE STUDY WITH MONTE CARLO METHOD
Abstract
The statistical assessment of electrical circuits, such as RLC circuits, is essential for understanding their behavior under uncertain conditions. In this paper, we explore the application of stochastic collocation (SC) as an alternative method for statistical assessment, comparing it with the widely used Monte Carlo method. SC offers an efficient approach for propagating uncertainties through complex mathematical models by approximating the solution at specified collocation points. We present a comparative study between SC and Monte Carlo in evaluating the statistical properties of RLC circuits, including parameters such as voltage, current, and power dissipation. Through numerical experiments, we demonstrate the accuracy and computational efficiency of SC in capturing the probabilistic behavior of RLC circuits, especially in scenarios with high-dimensional uncertainty. Additionally, we provide insights into the advantages and limitations of both methods, shedding light on their suitability for different applications in circuit analysis and design.
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