In this contribution, a new design framework leveraging Bayesian optimisation is developed to enhance the efficiency and quality of material and structural design processes. The proposed framework comprises two main steps. The first step involves efficiently exploring the design space with a minimum number of sampled points to mitigate computational costs. In the subsequent step, a composite Bayesian optimisation strategy is employed to evaluate the objective function and identify the next candidate for sampling. By building a surrogate model for numerical simulation responses in a fixed-size latent response space and using techniques like Principal Component Analysis for dimensionality reduction, the framework effectively exploits the composition aspect of the objective function. Unlike traditional methods that rely on random sampling across the design space, our Bayesian optimisation approach uses a dynamic, adaptive sampling strategy. This method significantly reduces the number of required experiments while effectively managing uncertainty. We evaluate the framework’s performance across various design scenarios and conduct a critical comparative analysis against well-established data-driven approaches. These scenarios include linear and nonlinear material and structural behaviours, addressing multi-objective optimisation and data variability. Our findings demonstrate substantial improvements in performance and quality, particularly in nonlinear settings. This underscores the framework’s potential to advance design methodologies in material and structural engineering.

中文翻译：

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用于材料和结构设计的复合贝叶斯优化框架


在这项贡献中，开发了一个利用贝叶斯优化的新设计框架，以提高材料和结构设计过程的效率和质量。拟议的框架包括两个主要步骤。第一步涉及以最少的采样点数量有效地探索设计空间，以降低计算成本。在后续步骤中，采用复合贝叶斯优化策略来评估目标函数并确定下一个采样候选者。通过在固定大小的潜在响应空间中构建数值模拟响应的代理模型，并使用主成分分析等技术进行降维，该框架有效地利用了目标函数的组成方面。与依赖于整个设计空间随机采样的传统方法不同，我们的贝叶斯优化方法使用动态的自适应采样策略。这种方法显著减少了所需的实验数量，同时有效地管理了不确定性。我们评估框架在各种设计场景中的性能，并针对成熟的数据驱动方法进行批判性的比较分析。这些场景包括线性和非线性材料和结构行为，解决多目标优化和数据可变性问题。我们的研究结果表明，性能和质量有了实质性的提高，特别是在非线性环境中。这凸显了该框架在推进材料和结构工程设计方法方面的潜力。