A common workflow for many engineering design problems requires the evaluation of the design system to be investigated under a range of conditions. These conditions usually involve a combination of several parameters. To perform a complete evaluation of a single candidate configuration, it may be necessary to perform hundreds to thousands of simulations. This can be computationally very expensive, particularly if several configurations need to be evaluated, as in the case of the mathematical optimization of a design problem. Although the simulations are extremely complex, generally, there is a high degree of redundancy in them, as many of the cases vary only slightly from one another. This redundancy can be exploited by omitting some simulations that are uninformative, thereby reducing the number of simulations required to obtain a reasonable approximation of the complete system. The decision of which simulations are useful is made through the use of machine learning techniques, which allow us to estimate the results of "yet-to-be-performed" simulations from the ones that are already performed. In this study, we present the results of one such technique, namely active learning, to provide an approximate result of an entire offshore riser design simulation portfolio from a subset that is 80% smaller than the original one. These results are expected to facilitate a significant speed-up in the offshore riser design.

中文翻译：

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通过基于池的主动学习自动选择仿真案例来加速工程设计

许多工程设计问题的通用工作流程需要在一系列条件下对设计系统进行评估。这些条件通常涉及几个参数的组合。要对单个候选配置进行完整评估，可能需要执行数百到数千次模拟。这在计算上可能非常昂贵，尤其是在需要评估多个配置的情况下，例如在设计问题的数学优化的情况下。尽管模拟极其复杂，但一般而言，其中存在高度冗余，因为许多情况彼此之间仅略有不同。可以通过省略一些无信息的模拟来利用这种冗余，从而减少了获得完整系统合理近似值所需的模拟次数。通过使用机器学习技术来决定哪些模拟有用，这使我们能够从已经执行的模拟中估计“尚未执行”的模拟结果。在这项研究中，我们展示了一种此类技术的结果，即主动学习，以从比原始设计小 80% 的子集提供整个海上立管设计模拟组合的近似结果。预计这些结果将促进海上立管设计的显着加速。