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Adaptive parametric sampling scheme for nonlinear model order reduction
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DOI：10.1007/s11071-021-07025-7.png)
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摘要
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Dynamical systems expressed as parameterized partial differential equations are ubiquitous in engineering and applied sciences. While standard model order reduction (MOR) techniques are not robust to parametric variations, the problem is exacerbated for large-scale problems with implicit parameter dependence. The matrix interpolatory MOR method has been recently extended to nonlinear systems; however, efficient sampling of the parametric space remains elusive. In order to enhance the approximating quality of the interpolated reduced models, this manuscript presents a generalized framework for nonlinear systems with an efficient sampling strategy for a two-dimensional parametric space. The proposed framework is substantiated through a suite of nonlinear benchmark models.
Keyword:
Model order reduction
MIMO systems
Parametric systems
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1.4W
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