Model order reduction for deforming domain problems in a time-continuous space-time setting

In the context of simulation-based methods, multiple challenges arise, two of which are considered in this work. As a first challenge, problems including time-dependent phenomena with complex domain deformations, potentially even with changes in the domain topology, need to be tackled appropriately. The second challenge arises when computational resources and the time for evaluating the model become critical in so-called many query scenarios for parametric problems. For example, these problems occur in optimization, uncertainty quantification (UQ), or automatic control, and using highly resolved full-order models (FOMs) may become impractical. To address both types of complexity, we present a novel projection-based model order reduction (MOR) approach for deforming domain problems that takes advantage of the time-continuous space-time formulation. We apply it to two examples that are relevant to engineering or biomedical applications and conduct an error and performance analysis. In both cases, we are able to drastically reduce the computational expense for a model evaluation and, at the same time, maintain an adequate accuracy level, each compared to the original time-continuous space-time full-order model (FOM). All in all, this work indicates the effectiveness of the presented MOR approach for deforming domain problems by taking advantage of a time-continuous space-time setting.

Automated summary

In this work, two challenges in simulation-based methods are discussed. The first challenge involves handling problems with complex domain deformations and changes in the domain topology. The second challenge arises in scenarios with parametric problems where computational resources and evaluation time are critical. To address these complexities, a novel projection-based model order reduction approach is presented, which takes advantage of the time-continuous space-time formulation. Two examples in engineering and biomedical applications are examined, showing the effectiveness of the approach in reducing computational expense while maintaining accuracy.