Surrogate models have several uses in engineering design, including speeding up design optimization, noise reduction, test measurement interpolation, gradient estimation, portability, and protection of intellectual property. Traditionally, surrogate models require that all training data conform to the same parametrization (e.g. design variables), limiting design freedom and prohibiting the reuse of historical data. In response, this paper proposes Graph-based Surrogate Models (GSMs) for trusses. The GSM can accurately predict displacement fields from static loads given the structure's geometry as input, enabling training across multiple parametrizations. GSMs build upon recent advancements in geometric deep learning which have led to the ability to learn on undirected graphs: a natural representation for trusses. To further promote flexible surrogate models, the paper explores transfer learning within the context of engineering design, and demonstrates positive knowledge transfer across data sets of different topologies, complexities, loads and applications, resulting in more flexible and data-efficient surrogate models for trusses.

中文翻译：

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迈向可重用代理模型：基于图的桁架迁移学习

代理模型在工程设计中有多种用途，包括加速设计优化、降噪、测试测量插值、梯度估计、可移植性和知识产权保护。传统上，代理模型要求所有训练数据符合相同的参数化（例如设计变量），限制了设计自由并禁止重复使用历史数据。作为回应，本文提出了用于桁架的基于图的代理模型 (GSM)。给定结构的几何形状作为输入，GSM 可以根据静态载荷准确预测位移场，从而实现跨多个参数化的训练。GSM 建立在几何深度学习的最新进展之上，这些进展导致了在无向图上学习的能力：桁架的自然表示。