model reduction

model reduction

[′mäd·əl ri′dək·shən]
(control systems)
The process of discarding certain modes of motion while retaining others in the model used by an active control system, in order that the control system can compute control commands with sufficient rapidity.
References in periodicals archive ?
The research of the group around Athanasios Antoulas focuses on dynamical systems and computation including model reduction of large-scale systems, such as Big Data.
Keywords: Chemical kinetics, Chemical equilibrium, Lyapunov function, Invariant manifolds, Model reduction.
A survey of model reduction methods for large-scale systems," Contemporary Mathematics, vol.
The proposed methodology builds on previously published Finite element based analysis to capture basic design features of the generator with three variables, and couples it with a model reduction technique to rapidly re-design the generator with desired fidelity.
This model reduction process is repeated for each least significant model parameter of the aerodynamic mathematical model until the error of the model exceeds a set threshold.
Summaries of lectures at a 2013 summer school in Shanghai cover a short course on matrix functions, a short course on exponential integrators, matrix equations and model reduction, Rayleigh quotient based on optimization methods for eigenvalue problems, and factorization-based sparse solvers and preconditioners.
The proper orthogonal decomposition method (POD) with radial basis function is a well-known model reduction method based on results of the studied phenomenon called the snapshot method.
MATHICSE has been one of the leading academic forces in the past ten years on the topic of model reduction, which is the central theme of this CTI project.
2]-optimal model reduction, this can slow down the convergence of iterative algorithms such as IRKA significantly; see [7].
These approaches employ model reduction with estimation.
The book then turns to methodological analysis, including structural identifiability, parameter sensitivity, parameter estimation, hypothesis testing, experiment optimization, and model reduction.
System-theoretic methods for model reduction of large-scale systems: Simulation, control, and inverse problems.

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