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Statistics > Machine Learning

Title: Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits

Abstract: We propose an active sampling flow, with the use-case of simulating the impact of combined variations on analog circuits. In such a context, given the large number of parameters, it is difficult to fit a surrogate model and to efficiently explore the space of design features.
By combining a drastic dimension reduction using sensitivity analysis and Bayesian surrogate modeling, we obtain a flexible active sampling flow. On synthetic and real datasets, this flow outperforms the usual Monte-Carlo sampling which often forms the foundation of design space exploration.
Comments: 7 pages
Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
Cite as: arXiv:2405.07971 [stat.ML]
  (or arXiv:2405.07971v1 [stat.ML] for this version)

Submission history

From: Reda Chhaibi [view email]
[v1] Mon, 13 May 2024 17:47:40 GMT (6365kb,D)

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