Model Order Reduction: Theory, Research Aspects and Applications
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Reduction of stiffness and mass matrices American Institute of Aeronautics and Astronautics Journal 3 2 : Houlston P. Extracting second order system matrices from state space system Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science 8 : Hughes P. Controllability and observability of linear matrix-second-order systems Journal of Applied Mechanics 47 2 : Koutsovasilis P. Comparison of model reduction techniques for large mechanical systems Multibody System Dynamics 20 2 : Reduction of large circuit models via low rank approximate gramians International Journal of Applied Mathematics and Computer Science 11 5 : Structure-preserving model reduction in J.
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Model-order reduction of lumped parameter systems via fractional calculus
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Tan S. Home About us Subjects Contacts. Advanced Search Help. Entire Site De Gruyter Online. Sign in Register.
English Deutsch. Extracting second-order structures from single-input state-space models: Application to model order reduction. Open access.
In particular, the focus will be on techniques that allow the use of preconditioned iterative methods for the solution of the inner linear systems, instead of sparse direct methods, and on methods that allow flexible shift-and-invert preconditioners for the outer Krylov iteration itself. The use of nonlinear semidefinite programming to remedy the loss of stability or passivity of reduced-order models generated by Lanczos-type algorithms will also be explored.
The proposed research is expected to lead to a more complete understanding of Krylov subspace-based order reduction and to result in original algorithms that are on par with their state-of-the-art counterparts for large eigenvalue problems and large linear systems. The use of computational techniques and numerical simulation is ubiquitous in the design and verification of today's complex engineering systems.
For example, a state-of-the-art computer computer chip contains about one billion transistors.
Despite this enormous complexity, the design and verification of such chips is done almost exclusively with simulation, and first-time-correct fabrication in silicon is the norm. However, even with today's computing power, simulation of a complete system is often not feasible due to the extremely high dimension of the mathematical model describing the system.
Order reduction is a key technology to make such simulation tasks possible by first replacing the original model by a suitable approximation of much smaller dimension. The proposed research is expected to lead to new order-reduction techniques that will have applications in many important areas, including the design of computer chips, microelectromechanical systems, nanotechnology, and structural dynamics. Some full text articles may not yet be available without a charge during the embargo administrative interval.
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