Volume 14, Issue 3
Subspace Trajectory Piecewise-Linear Model Order Reduction for Nonlinear Circuits

Xiaoda Pan, Hengliang Zhu, Fan Yang & Xuan Zeng

Commun. Comput. Phys., 14 (2013), pp. 639-663.

Published online: 2013-09

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  • Abstract

Despite the efficiency of trajectory piecewise-linear (TPWL) model order reduction (MOR) for nonlinear circuits, it needs large amount of expansion points for large-scale nonlinear circuits. This will inevitably increase the model size as well as the simulation time of the resulting reduced macromodels. In this paper, subspace TPWL-MOR approach is developed for the model order reduction of nonlinear circuits. By breaking the high-dimensional state space into several subspaces with much lower dimensions, the subspace TPWL-MOR has very promising advantages of reducing the number of expansion points as well as increasing the effective region of the reduced-order model in the state space. As a result, the model size and the accuracy of the TWPL model can be greatly improved. The numerical results have shown dramatic reduction in the model size as well as the improvement in accuracy by using the subspace TPWL-MOR compared with the conventional TPWL-MOR approach.


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@Article{CiCP-14-639, author = {}, title = {Subspace Trajectory Piecewise-Linear Model Order Reduction for Nonlinear Circuits}, journal = {Communications in Computational Physics}, year = {2013}, volume = {14}, number = {3}, pages = {639--663}, abstract = {

Despite the efficiency of trajectory piecewise-linear (TPWL) model order reduction (MOR) for nonlinear circuits, it needs large amount of expansion points for large-scale nonlinear circuits. This will inevitably increase the model size as well as the simulation time of the resulting reduced macromodels. In this paper, subspace TPWL-MOR approach is developed for the model order reduction of nonlinear circuits. By breaking the high-dimensional state space into several subspaces with much lower dimensions, the subspace TPWL-MOR has very promising advantages of reducing the number of expansion points as well as increasing the effective region of the reduced-order model in the state space. As a result, the model size and the accuracy of the TWPL model can be greatly improved. The numerical results have shown dramatic reduction in the model size as well as the improvement in accuracy by using the subspace TPWL-MOR compared with the conventional TPWL-MOR approach.


}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.070512.051112a}, url = {http://global-sci.org/intro/article_detail/cicp/7176.html} }
TY - JOUR T1 - Subspace Trajectory Piecewise-Linear Model Order Reduction for Nonlinear Circuits JO - Communications in Computational Physics VL - 3 SP - 639 EP - 663 PY - 2013 DA - 2013/09 SN - 14 DO - http://dor.org/10.4208/cicp.070512.051112a UR - https://global-sci.org/intro/article_detail/cicp/7176.html KW - AB -

Despite the efficiency of trajectory piecewise-linear (TPWL) model order reduction (MOR) for nonlinear circuits, it needs large amount of expansion points for large-scale nonlinear circuits. This will inevitably increase the model size as well as the simulation time of the resulting reduced macromodels. In this paper, subspace TPWL-MOR approach is developed for the model order reduction of nonlinear circuits. By breaking the high-dimensional state space into several subspaces with much lower dimensions, the subspace TPWL-MOR has very promising advantages of reducing the number of expansion points as well as increasing the effective region of the reduced-order model in the state space. As a result, the model size and the accuracy of the TWPL model can be greatly improved. The numerical results have shown dramatic reduction in the model size as well as the improvement in accuracy by using the subspace TPWL-MOR compared with the conventional TPWL-MOR approach.


Xiaoda Pan, Hengliang Zhu, Fan Yang & Xuan Zeng. (2020). Subspace Trajectory Piecewise-Linear Model Order Reduction for Nonlinear Circuits. Communications in Computational Physics. 14 (3). 639-663. doi:10.4208/cicp.070512.051112a
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