An Adaptive Sub-Domain Framework for Parametric Order Reduction

Methods of parametric order reduction are very appealing for solving parameter-dependent models at the fields level, because they provide fast simulations and low systematic error. This paper presents a self-adaptive framework for computing reduced-order models featuring affine and non-affine parameters. It is based on a hypercube partitioning of the domain of non-affine parameters and employs non-uniform grid refinement, controlled by a suitable error indicator. Compared with state-of-the-art entire-domain methods, the proposed sub-domain approach achieves significant improvements in memory consumption and computer run time.