Sentence examples for model reduction approaches from inspiring English sources

In the current paper, we discuss how these global model reduction approaches can be combined with local techniques to speed-up the simulations.

This is a conceptually new development of model reduction approaches, which we think will stimulate much further research and will prove to be very useful in future modelling projects.

The method is a generalization of the well-known model reduction approach for systems with input delay.

In this paper, we develop a multiscale model reduction approach to couple gas flow and geomechanics in fractured shale media.

This paper presents a developed model reduction approach for the vibration analysis of hydraulic pipeline with long distance and multi-supports with elasticity popularly used in aircraft.

This is a model reduction approach called modal reduction which, if efficiently implemented, can save a huge amount of computing time and storage, despite the fact that it is partially heuristic.

The model reduction approach is further tested by performing patient simulations under exercise and varying degrees of pathophysiological conditions based on reduction of reference solutions (rest and medium backflow conditions respectively).

We demonstrate the model reduction approach with polycrystal microstructures and compute the variability of homogenized properties using a sparse grid collocation approach in the reduced-order space that describes the grain size and orientation variability.

In this paper a dedicated recurrent neural network design and a model reduction approach are proposed in order to improve the balance between complexity and quality of black box nonlinear system identification models.

The present work considers a model reduction approach for fault diagnosis with generalised likelihood ratio (GLR) method to improve upon its diagnostic performance and computational efficiency in large dimensional applications.

With this model reduction approach, it was found that the state variables can be accurately predicted by projecting the original modeling equations onto the reduced basis with considerable computational savings over the original simulations.