Results 141 to 150 of about 6,100,810 (194)

Laguerre-SVD reduced order modeling

IEEE 8th Topical Meeting on Electrical Performance of Electronic Packaging (Cat. No.99TH8412), 2000
A reduced-order modeling method based on a system description in terms of orthonormal Laguerre functions, together with a Krylov subspace decomposition technique is presented. The link with Pade approximation, the block Arnoldi process and singular value decomposition (SVD) leads to a simple and stable implementation of the algorithm. Novel features of
L. Knockaert, D. De Zutter
openaire   +1 more source

Reduced Order Stochastic Models

1988 American Control Conference, 1988
This paper presents an approach to reduce the order of large-scale stochastic systems. The reduced-order model is obtained by considering only the stable modes through optimization of a steady-state error. Examples are given to illustrate the proposed method.
Craig S. Sims, Ali Feliachi
openaire   +1 more source

Reduced-Order Modeling

2005
Abstract In recent years, reduced-order modeling techniques have proven to be powerful tools for various problems in circuit simulation. For example, today, reduction techniques are routinely used to replace the large RCL subcircuits that model the interconnect or the pin package of VLSI circuits by models of much smaller dimension.
Zhaojun Bai   +2 more
openaire   +1 more source

Reduced-Order Modeling

2014
In this chapter, the full-order state-space models presented in Chap. 3 are reduced in order and parametrized in the main parameters of the flight envelope. Order reduction is achieved by a multistep procedure: A modal reduction is followed by a reduction of the complete aeroelastic model and finally a balanced reduction is performed.
M. Valášek   +3 more
openaire   +1 more source

Space‐local reduced‐order bases for accelerating reduced‐order models through sparsity

International Journal for Numerical Methods in Engineering, 2022
AbstractProjection‐based model order reduction (PMOR) methods based on linear or affine approximation subspaces accelerate numerical predictions by reducing the dimensionality of the underlying computational models. The state of the art of PMOR includes approximation methods based on state‐local subspaces—that is, subspaces associated with different ...
Spenser Anderson   +2 more
openaire   +1 more source

Home - About - Disclaimer - Privacy