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Generation of stochastic interconnect responses via gaussian process latent variable models

Simon De Ridder (UGent) , Dirk Deschrijver (UGent) , Paolo Manfredi (UGent) , Tom Dhaene (UGent) and Dries Vande Ginste (UGent)
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Abstract
We introduce a novel generative model for stochastic device responses using limited available data. This model is oblivious to any varying design parameters or their distribution and only requires a small set of "training" responses. Using this model, new responses are efficiently generated whose distribution closely matches that of the real data, e.g., for use in Monte-Carlo-like analyses. The modeling methodology consists of a vector fitting step, where device responses are represented by a rational model, followed by the optimization of a Gaussian process latent variable model. Passivity is guaranteed by a posteriori discarding of nonpassive responses. The novel model is shown to considerably outperform a previous generative model, as evidenced by comparing accuracies of distribution estimation for the case of differential-to-common mode conversion in two coupled microstrip lines.
Keywords
UNCERTAINTY QUANTIFICATION, Gaussian process latent variable model (GP-LVM), generative models, high-speed connectors and links, statistical link analysis, stochastic, modeling

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Chicago
De Ridder, Simon, Dirk Deschrijver, Paolo Manfredi, Tom Dhaene, and Dries Vande Ginste. 2019. “Generation of Stochastic Interconnect Responses via Gaussian Process Latent Variable Models.” Ieee Transactions on Electromagnetic Compatibility 61 (2): 582–585.
APA
De Ridder, Simon, Deschrijver, D., Manfredi, P., Dhaene, T., & Vande Ginste, D. (2019). Generation of stochastic interconnect responses via gaussian process latent variable models. IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, 61(2), 582–585.
Vancouver
1.
De Ridder S, Deschrijver D, Manfredi P, Dhaene T, Vande Ginste D. Generation of stochastic interconnect responses via gaussian process latent variable models. IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY. Piscataway: Ieee-inst Electrical Electronics Engineers Inc; 2019;61(2):582–5.
MLA
De Ridder, Simon et al. “Generation of Stochastic Interconnect Responses via Gaussian Process Latent Variable Models.” IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 61.2 (2019): 582–585. Print.
@article{8601765,
  abstract     = {We introduce a novel generative model for stochastic device responses using limited available data. This model is oblivious to any varying design parameters or their distribution and only requires a small set of "training" responses. Using this model, new responses are efficiently generated whose distribution closely matches that of the real data, e.g., for use in Monte-Carlo-like analyses. The modeling methodology consists of a vector fitting step, where device responses are represented by a rational model, followed by the optimization of a Gaussian process latent variable model. Passivity is guaranteed by a posteriori discarding of nonpassive responses. The novel model is shown to considerably outperform a previous generative model, as evidenced by comparing accuracies of distribution estimation for the case of differential-to-common mode conversion in two coupled microstrip lines.},
  author       = {De Ridder, Simon and Deschrijver, Dirk and Manfredi, Paolo and Dhaene, Tom and Vande Ginste, Dries},
  issn         = {0018-9375},
  journal      = {IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY},
  keywords     = {UNCERTAINTY QUANTIFICATION,Gaussian process latent variable model (GP-LVM),generative models,high-speed connectors and links,statistical link analysis,stochastic,modeling},
  language     = {eng},
  number       = {2},
  pages        = {582--585},
  publisher    = {Ieee-inst Electrical Electronics Engineers Inc},
  title        = {Generation of stochastic interconnect responses via gaussian process latent variable models},
  url          = {http://dx.doi.org/10.1109/TEMC.2018.2830104},
  volume       = {61},
  year         = {2019},
}

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