Modern simulation scenarios require real-time or many-query responses from a simulation model. This is the driving force for increased efforts in model order reduction for high-dimensional dynamical systems or partial differential equations. This demand for fast simulation models is even more critical for parameterized problems. Several snapshot-based methods for basis construction exist for parameterized model order reduction, for example, proper orthogonal decomposition or reduced basis methods. They require the careful choice of samples for generation of the reduced model. In this article we address two types of grid-based adaptivity that can be beneficial in such basis generation procedures. First, we describe an approach for training set adaptivity. Second, we introduce an approach for multiple bases on adaptive parameter domain partitions. Due to the modularity, both methods also can easily be combined. They result in efficient reduction schemes with accelerated training times, improved approximation properties and control on the reduced basis size. We demonstrate the applicability of the approaches for instationary partial differential equations and parameterized dynamical systems.

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An Interactive Genetic Algorithm is proposed to progressively sketch the desired side-view of a car profile. It adopts a Fourier decomposition of a 2D profile as the genotype, and proposes a cross-over mechanism. In addition, a formula function of two genes' discrepancies is fitted to the perceived dissimilarity between two car profiles. This similarity index is intensively used, throughout a series of user tests, to highlight the added value of the IGA compared to a systematic car shape exploration, to prove its ability to create superior satisfactory designs and to stimulate designer's creativity. These tests have involved six designers with a design goal defined by a semantic attribute. The results reveal that if "friendly" is diversely interpreted in terms of car shapes, "sportive" denotes a very conventional representation which may be a limitation for shape renewal.

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The Kalman filter is a widely known tool in control theory for estimating the state of a linear system disturbed by noise. However, when applying the Kalman filter on systems described by parametrerized partial differential equations (PPDEs) the calculation of state estimates can take an excessive amount of time and real-time state estimation may be infeasible. In this work we derive a low dimensional representation of a parameter dependent Kalman filter for PPDEs via the reduced basis method. Thereby rapid state estimation, and in particular the rapid estimation of a linear output of interest, will be feasible. We will also derive a posteriori error bounds for evaluating the quality of the output estimations. Furthermore we will show how to verify the stability of the filter using an observability condition. We will demonstrate the performance of the reduced order Kalman filter and the error bounds with a numerical example modeling the heat transfer in a plate.