Model Reduction and Simulation of Nonlinear Circuits via Tensor Decomposition
Author(s)Haotian Liu; Daniel, Luca; Ngai Wong, Luca
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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 algorithm we named TNMOR 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 reduced-order model. Therefore, TNMOR completely avoids the dense reduced-order system matrices, which in turn allows faster simulation and a smaller memory requirement if relatively low-rank 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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Institute of Electrical and Electronics Engineers (IEEE)
Haotian Liu, Luca Daniel, and Ngai Wong. “Model Reduction and Simulation of Nonlinear Circuits via Tensor Decomposition.” IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 34, no. 7 (July 2015): 1059–1069.
Author's final manuscript