A new algorithm for L2 optimal model reduction
Automatica (Journal of IFAC)
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Proceedings of the 40th annual Design Automation Conference
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SIAM Journal on Matrix Analysis and Applications
Parameter independent model order reduction
Mathematics and Computers in Simulation
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ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
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$\mathcal{H}_2$ Model Reduction for Large-Scale Linear Dynamical Systems
SIAM Journal on Matrix Analysis and Applications
Efficient block-based parameterized timing analysis covering all potentially critical paths
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
h2-norm optimal model reduction for large scale discrete dynamical MIMO systems
Journal of Computational and Applied Mathematics
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
First-Order Incremental Block-Based Statistical Timing Analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient linear circuit analysis by Pade approximation via the Lanczos process
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Parametric dominant pole algorithm for parametric model order reduction
Journal of Computational and Applied Mathematics
Model reduction of linear time-varying systems over finite horizons
Applied Numerical Mathematics
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We provide a unifying projection-based framework for structure-preserving interpolatory model reduction of parameterized linear dynamical systems, i.e., systems having a structured dependence on parameters that we wish to retain in the reduced-order model. The parameter dependence may be linear or nonlinear and is retained in the reduced-order model. Moreover, we are able to give conditions under which the gradient and Hessian of the system response with respect to the system parameters is matched in the reduced-order model. We provide a systematic approach built on established interpolatory $\mathcal{H}_2$ optimal model reduction methods that will produce parameterized reduced-order models having high fidelity throughout a parameter range of interest. For single input/single output systems with parameters in the input/output maps, we provide reduced-order models that are optimal with respect to an $\mathcal{H}_2\otimes\mathcal{L}_2$ joint error measure. The capabilities of these approaches are illustrated by several numerical examples from technical applications.