The last decades have been driven by significant progress in the computational capacity, which have been supporting the development of increasingly realistic and detailed simulations. However, despite these improvements, several problems still do not have an effective solution, due to their numerical complexity. As a result, the answer to these problems can be improved by using techniques that enable the description of phenomena with less resolution, but with lower computational costs, which is the case of the reduced order models. The main objective of this article is the presentation of a new approach for reduced order model development and application in the design and optimization of structural parts. The selected method is the artificial neural networks. Artificial neural networks allow the prediction of certain variables based on a given dataset. Two typical case studies are addressed: the first is a fixed plate subjected to uniformly distributed pressure and the second is a reinforced panel also subjected to internal pressure, with regular reinforcements to improve the specific strength. With this method, a substantial reduction in the simulation time is observed, being, approximately, 40 times faster than the solution obtained with Ansys. The developed neural network has a relative average difference of about 20 %, which is considered satisfactory given the complexity of the problem and considering it is a first application of these networks in this domain. In conclusion, this research made it possible to highlight the potential of reduced order model: including the shorter response time, the less computational resources, and the simplification of problems in detriment of less resolution in the description of structural behaviour. Given these advantages, it is expected that these models will play a key role in future applications, as in digital twins.

在过去的几十年中，计算能力取得了显着进步，这些进步一直在支持日益逼真和详细的仿真的发展。但是，尽管进行了这些改进，但由于数值上的复杂性，一些问题仍然没有有效的解决方案。结果，可以通过使用以下技术来改善对这些问题的答案：该技术能够以较低的分辨率描述现象，但是以较低的计算成本进行描述，这是降阶模型的情况。本文的主要目的是介绍一种新的方法，用于在结构零件的设计和优化中开发降阶模型。选择的方法是人工神经网络。人工神经网络允许根据给定的数据集预测某些变量。解决了两个典型的案例研究：第一个是承受均匀分布压力的固定板，第二个是还承受内部压力的加固板，并通过定期加固以提高比强度。使用这种方法，观察到的仿真时间大大减少，比用Ansys获得的解决方案快约40倍。发达的神经网络具有约20％的相对平均差异，考虑到问题的复杂性，并且考虑到这是这些网络在该领域的首次应用，这被认为是令人满意的。总而言之，这项研究有可能强调降阶模型的潜力：包括更短的响应时间，较少的计算资源，简化了结构行为描述中不利于降低分辨率的问题。鉴于这些优势，预计这些模型将在未来的应用中扮演重要角色，例如在数字双胞胎中。