Reduced order models (ROM) are commonly employed to solve parametric problems and to devise inexpensive response surfaces to evaluate quantities of interest in real-time. There are many families of ROMs in the literature and choosing among them is not always a trivial task. This work presents a comparison of the performance of a priori and a posteriori proper generalised decomposition (PGD) algorithms for an incompressible Stokes flow problem in a geometrically parametrised domain. This problem is particularly challenging as the geometric parameters affect both the solution manifold and the computational spatial domain. The difficulty is further increased because multiple geometric parameters are considered and extended ranges of values are analysed for the parameters and this leads to significant variations in the flow features. Using a set of numerical experiments involving geometrically parametrised microswimmers, the two PGD algorithms are extensively compared in terms of their accuracy and their computational cost, expressed as a function of the number of full-order solves required.

降阶模型 (ROM) 通常用于解决参数问题并设计廉价的响应面以实时评估感兴趣的数量。文献中有许多 ROM 家族，在其中进行选择并不总是一项微不足道的任务。这项工作对几何参数化域中不可压缩斯托克斯流问题的先验和后验适当广义分解 (PGD) 算法的性能进行了比较。这个问题特别具有挑战性，因为几何参数会影响解流形和计算空间域。由于考虑了多个几何参数并分析了参数值的扩展范围，因此难度进一步增加，这导致流动特征的显着变化。