Efficient Regression-Based Polynomial Chaos using Adjoint Sensitivity
| Autor: | Marco T. Kassis, Karanvir S. Sidhu, Roni Khazaka |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Polynomial chaos
020209 energy Computation Monte Carlo method MathematicsofComputing_NUMERICALANALYSIS 020206 networking & telecommunications 02 engineering and technology Derivative Regression CHAOS (operating system) 0202 electrical engineering electronic engineering information engineering Applied mathematics Sensitivity (control systems) Random variable Mathematics |
| Zdroj: | 2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS). |
| DOI: | 10.1109/epeps47316.2019.193229 |
| Popis: | In this paper, we propose a derivative based method for efficient circuit variability analysis using Polynomial Chaos framework. The sensitivity of the stochastic output with respect to the random input is computed using the adjoint sensitivity method and used in the computation of the polynomial chaos coefficients. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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