Physics-Inspired Neural Networks in the Evaluation, Generation and Design of Frame Structures

物理启发的神经网络在框架结构的评估、生成和设计中的应用

• 批准号: 523871886
• 负责人: Professor Dr.-Ing. Sandro Wartzack
• 金额: --
• 依托单位: Lehrstuhl für Konstruktionstechnik (KTmfk)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/523871886?language=en
• 关键词: Physics; Inspired; Neural; Networks; Evaluation

Structural optimization represents an economical and effective lightweight design method, especially when full material utilization in terms of strength and stiffness is desired. The design and evaluation of truss structures is one of the most common tasks in practice, often by using numerical simulation with beam or truss elements. In this work, alternative design and evaluation procedures of such 1D idealizations based on so-called physical-inspired neural networks (PINN) are the focus of research. Thereby, mainly 3D simulation data and 3D topology optimization results shall serve as a training basis to improve the predictive behaviour of the 1D idealizations. In total, three different PINNs will be investigated. The first PINN is expected to lead to improved prediction of physical quantities such as deformation and strain of 1D models. The second PINN is intended to derive optimal cross-section parameters based on a given 1D frame structure. The third PINN will use training data from 3D optimizations to predict optimal design proposals for frame structures so that, for example, regions with multi-axial states can be directly optimized and derived as a parametric model without the need for complex topology optimization. In addition to the training of PINNs, a method based on the so-called skeletonization for the fully automatic transfer of results from a 3D simulation to a 1D model is also investigated. This fully automatic transfer is necessary to generate the synthetic data sets for the respective PINNs. Finally, the trained PINNs are combined to realize an automated evaluation, cross-section dimensioning and locally optimized regions in real time (a few seconds) for a bicycle frame, for example.

结构优化代表了一种经济有效的轻质设计方法，尤其是在需要实力和刚度的全面利用时。桁架结构的设计和评估通常是使用梁或桁架元素的数值模拟，是实践中最常见的任务之一。在这项工作中，基于所谓的物理启发的神经网络（PINN）的此类1D理想化的替代设计和评估程序是研究的重点。因此，主要是3D仿真数据和3D拓扑优化结果应作为培训基础，以改善一维理想化的预测行为。总共将研究三个不同的Pinn。预计第一个PINN将导致改进物理量的预测，例如变形和1D模型的应变。第二个PINN旨在根据给定的1D帧结构得出最佳的横截面参数。第三个PINN将使用来自3D优化的训练数据来预测框架结构的最佳设计建议，例如，具有多轴状态的区域可以直接优化并作为参数模型得出，而无需进行复杂的拓扑优化。除了训练PINN，还研究了一种基于所谓的骨架化方法，用于将结果从3D模拟转移到1D模型。对于生成相应的PINNS的合成数据集是必需的。最后，将训练有素的PINN组合在一起，以实现自动化的自动化评估，横截面尺寸和本地优化的区域（例如，几秒钟）为自行车框架。