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.
Generation of stochastic interconnect responses via Gaussian process latent variable models / De Ridder, S.; Deschrijver, D.; Manfredi, P.; Dhaene, T.; Ginste, D. V.. - In: IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY. - ISSN 0018-9375. - STAMPA. - 61:2(2019), pp. 582-585.
Titolo: | Generation of stochastic interconnect responses via Gaussian process latent variable models |
Autori: | |
Data di pubblicazione: | 2019 |
Rivista: | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/TEMC.2018.2830104 |
Appare nelle tipologie: | 1.1 Articolo in rivista |
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jnl-2019-TEMC-letters.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2759721