Generation of stochastic interconnect responses via gaussian process latent variable models
- Author
- Simon De Ridder, Dirk Deschrijver (UGent) , Paolo Manfredi (UGent) , Tom Dhaene (UGent) and Dries Vande Ginste (UGent)
- Organization
- 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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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8601765
- MLA
- De Ridder, Simon, et al. “Generation of Stochastic Interconnect Responses via Gaussian Process Latent Variable Models.” IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, vol. 61, no. 2, Ieee-inst Electrical Electronics Engineers Inc, 2019, pp. 582–85, doi:10.1109/TEMC.2018.2830104.
- APA
- De Ridder, S., 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. https://doi.org/10.1109/TEMC.2018.2830104
- Chicago author-date
- 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): 582–85. https://doi.org/10.1109/TEMC.2018.2830104.
- Chicago author-date (all authors)
- 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. doi:10.1109/TEMC.2018.2830104.
- 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. 2019;61(2):582–5.
- IEEE
- [1]S. De Ridder, D. Deschrijver, P. Manfredi, T. Dhaene, and D. Vande Ginste, “Generation of stochastic interconnect responses via gaussian process latent variable models,” IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, vol. 61, no. 2, pp. 582–585, 2019.
@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://doi.org/10.1109/TEMC.2018.2830104}}, volume = {{61}}, year = {{2019}}, }
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