An adaptive least angle regression method for uncertainty quantification in FDTD computation

Runze Hu, Fumie Costen, Vikass Monebhurrun, Ryutaro Himeno, Hideo Yokota

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    The non-intrusive polynomial chaos (NIPC) expansion method is used to quantify the uncertainty of a stochastic system. It potentially reduces the number of numerical simulations in modelling process, thus improving efficiency, whilst
    ensuring accuracy. However, the number of polynomial bases grows substantially with the increase of random parameters, which may render the technique ineffective due to the excessive computational resources. To address such problems, methods based on the sparse strategy such as the least angle regression (LARS) method with hyperbolic index sets can be used. This paper presents the first work to improve the accuracy of the original LARS method for uncertainty quantification (UQ). We propose an adaptive LARS method in order to quantify the uncertainty of the results from the numerical simulations with
    higher accuracy than the original LARS method. The proposed method outperforms the original LARS method in terms of accuracy and stability. The L2 regularisation scheme further reduces the number of input samples while maintaining the accuracy of the LARS method.
    Original languageEnglish
    JournalIEEE Transactions on Antennas and Propagation
    Early online date26 Sept 2018
    Publication statusPublished - 2018


    • Non-intrusive polynomial chaos (NIPC) expansion
    • least angle regression (LARS)
    • uncertainty quantification (UQ)
    • Finite difference time domain (FDTD)
    • Debye media

    Research Beacons, Institutes and Platforms

    • Manchester Institute for Collaborative Research on Ageing


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