  This contribution presents a model order reduction framework for real-time
efficient solution of trimmed, multi-patch isogeometric Kirchhoff-Love shells.
In several scenarios, such as design and shape optimization, multiple
simulations need to be performed for a given set of physical or geometrical
parameters. This step can be computationally expensive in particular for real
world, practical applications. We are interested in geometrical parameters and
take advantage of the flexibility of splines in representing complex
geometries. In this case, the operators are geometry-dependent and generally
depend on the parameters in a non-affine way. Moreover, the solutions obtained
from trimmed domains may vary highly with respect to different values of the
parameters. Therefore, we employ a local reduced basis method based on
clustering techniques and the Discrete Empirical Interpolation Method to
construct affine approximations and efficient reduced order models. In
addition, we discuss the application of the reduction strategy to parametric
shape optimization. Finally, we demonstrate the performance of the proposed
framework to parameterized Kirchhoff-Love shells through benchmark tests on
trimmed, multi-patch meshes including a complex geometry. The proposed approach
is accurate and achieves a significant reduction of the online computational
cost in comparison to the standard reduced basis method.
