An adaptive multi-element probabilistic collocation method for statistical EMC/EMI characterization

Abdulkadir C. Yücel, Hakan Bagci, Eric Michielssen

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


An adaptive multi-element probabilistic collocation (ME-PC) method for quantifying uncertainties in electromagnetic compatibility and interference phenomena involving electrically large, multi-scale, and complex platforms is presented. The method permits the efficient and accurate statistical characterization of observables (i.e., quantities of interest such as coupled voltages) that potentially vary rapidly and/or are discontinuous in the random variables (i.e., parameters that characterize uncertainty in a system's geometry, configuration, or excitation). The method achieves its efficiency and accuracy by recursively and adaptively dividing the domain of the random variables into subdomains using as a guide the decay rate of relative error in a polynomial chaos expansion of the observables. While constructing local polynomial expansions on each subdomain, a fast integral-equation-based deterministic field-cable-circuit simulator is used to compute the observable values at the collocation/integration points determined by the adaptive ME-PC scheme. The adaptive ME-PC scheme requires far fewer (computationally costly) deterministic simulations than traditional polynomial chaos collocation and Monte Carlo methods for computing averages, standard deviations, and probability density functions of rapidly varying observables. The efficiency and accuracy of the method are demonstrated via its applications to the statistical characterization of voltages in shielded/unshielded microwave amplifiers and magnetic fields induced on car tire pressure sensors. © 2013 IEEE.
Original languageEnglish (US)
Pages (from-to)1154-1168
Number of pages15
JournalIEEE Transactions on Electromagnetic Compatibility
Issue number6
StatePublished - Dec 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the National Science Foundation under Grant DMS 0713771, AFOSR/NSSEFF Program Award FA9550-10-1-0180, Sandia Grant "Development of Calderon Multiplicative Preconditioners with Method of Moments Algorithms," KAUST Grant 399813, ONR BRC Grant "Randomized Algorithms for Reduced Representations," and Center for Uncertainty Quantification in Computational Science and Engineering at KAUST.

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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