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- W2079735689 abstract "In this paper, we consider the topic of model reduction for nonlinear dynamical systems based on kernel expansions. Our approach allows for a full offline/online decomposition and efficient online computation of the reduced model. In particular, we derive an a-posteriori state-space error estimator for the reduction error. A key ingredient is a local Lipschitz constant estimation that enables rigorous a-posteriori error estimation. The computation of the error estimator is realized by solving an auxiliary differential equation during online simulations. Estimation iterations can be performed that allow a balancing between estimation sharpness and computation time. Numerical experiments demonstrate the estimation improvement over different estimator versions and the rigor and effectiveness of the error bounds." @default.
- W2079735689 created "2016-06-24" @default.
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- W2079735689 date "2012-01-01" @default.
- W2079735689 modified "2023-09-27" @default.
- W2079735689 title "Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems" @default.
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- W2079735689 doi "https://doi.org/10.1016/j.sysconle.2011.10.012" @default.
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