ARMS - automatic residue-minimization based sampling for multi-point modeling techniques
Proceedings of the 46th Annual Design Automation Conference
Linearized reduced-order models for subsurface flow simulation
Journal of Computational Physics
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Automated compact dynamical modeling: an enabling tool for analog designers
Proceedings of the 47th Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Enhanced linearized reduced-order models for subsurface flow simulation
Journal of Computational Physics
Interpolatory Projection Methods for Parameterized Model Reduction
SIAM Journal on Scientific Computing
Model order reduction of fully parameterized systems by recursive least square optimization
Proceedings of the International Conference on Computer-Aided Design
Journal of Electronic Testing: Theory and Applications
Mathematics and Computers in Simulation
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This paper presents a parameterized reduction technique for highly nonlinear systems. In our approach, we first approximate the nonlinear system with a convex combination of parameterized linear models created by linearizing the nonlinear system at points along training trajectories. Each of these linear models is then projected using a moment-matching scheme into a low-order subspace, resulting in a parameterized reduced-order nonlinear system. Several options for selecting the linear models and constructing the projection matrix are presented and analyzed. In addition, we propose a training scheme which automatically selects parameter-space training points by approximating parameter sensitivities. Results and comparisons are presented for three examples which contain distributed strong nonlinearities: a diode transmission line, a microelectromechanical switch, and a pulse-narrowing nonlinear transmission line. In most cases, we are able to accurately capture the parameter dependence over the parameter ranges of plusmn50% from the nominal values and to achieve an average simulation speedup of about 10x.