Name
Abstract | ||
|---|---|---|
Model order reduction of nonlinear circuits (especially highly nonlinear circuits), has always been a theoretically and numerically challenging task. In this paper we utilize tensors (namely, a higher order generalization of matrices) to develop a tensor-based nonlinear model order reduction (TNMOR) algorithm for the efficient simulation of nonlinear circuits. Unlike existing nonlinear model order reduction methods, in TNMOR high-order nonlinearities are captured using tensors, followed by decomposition and reduction to a compact tensor-based reducedorder model. Therefore, TNMOR completely avoids the dense reduced-order system matrices, which in turn allows faster simulation and a smaller memory requirement if relatively lowrank approximations of these tensors exist. Numerical experiments on transient and periodic steady-state analyses confirm the superior accuracy and efficiency of TNMOR, particularly in highly nonlinear scenarios. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCAD.2015.2409272 | IEEE Trans. on CAD of Integrated Circuits and Systems |
Keywords | DocType | Volume |
tensor,nonlinear model order reduction,reducedorder model | Journal | PP |
Issue | ISSN | Citations |
99 | 0278-0070 | 5 |
PageRank | References | Authors |
0.46 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liu, H. | 1 | 5 | 0.46 |
| Daniel, L. | 2 | 5 | 0.46 |
| Ngai Wong | 3 | 321 | 58.74 |