面向目标非线性机械响应的细胞复合材料逆向设计：基于多保真度贝叶斯优化
Inverse Design of Cellular Composites for Targeted Nonlinear Mechanical Response via Multi-Fidelity Bayesian Optimisation
AI总结：
本文提出一种多保真度贝叶斯优化框架，用于设计具有目标非线性机械响应的细胞复合材料，通过整合多保真度信息并利用相似性评分，有效探索设计空间，减少高成本评估依赖，验证了在短碳纤维增强PET-G复合材料压缩测试中的有效性。
摘要：
机器学习和增材制造的兴起使设计具有定制性能的结构材料成为可能，其性能超越天然材料。逆向设计为试错方法提供了数据高效的替代方案，但大多数现有方法依赖于大规模数据集或稀缺的高保真度数据来自模拟和实验。这些要求对具有非线性机械响应的结构材料提出了特别的挑战，因为捕捉复杂的变形模式需要昂贵的评估。为此，提出了一种多保真度贝叶斯优化（MFBO）框架，用于逆向设计直接针对其完整非线性响应的细胞复合材料。通过整合多个保真度来源的信息并使用相似性评分对响应进行标量化，该框架能够在减少对昂贵评估依赖的同时，高效地探索设计空间。作为概念验证，该方法应用于旋转变形细胞复合材料，通过有限元模型验证，并通过短碳纤维增强PET-G复合材料的压缩测试进行验证。考虑了四种目标响应，三种多保真度策略与标准单保真度方法进行了基准测试。在所有情况下，MFBO实现了更高的相似性评分，并一致地恢复了目标响应，优于相同评估预算下的单保真度基线方法，同时成功恢复了所有目标响应。这些结果证明了MFBO在逆向设计随机结构材料中的有效性，其中高质量数据稀缺但低成本代理存在。通过高效地导航复杂的设计空间，MFBO能够创建具有精确定制非线性机械行为的细胞复合材料。
英文摘要：
The rise of machine learning and additive manufacturing has enabled the design of architected materials with tailored properties that surpass those of natural materials. Inverse design offers a data-efficient alternative to trial-and-error methods, yet most existing approaches depend on either large datasets or scarce high-fidelity data from simulations and experiments. These requirements pose a particular challenge for architected materials with nonlinear mechanical responses, where capturing complex deformation modes requires expensive evaluations. To address this, a Multi-Fidelity Bayesian Optimisation (MFBO) framework for the inverse design of cellular composites that directly targets their full nonlinear response is introduced. By integrating information from multiple fidelity sources and scalarising the response using a similarity score, the framework enables efficient exploration of the design space while reducing reliance on costly evaluations. As a proof of concept, the method is applied to spinodoid cellular composites using finite element models, validated with compression tests on short carbon-fibre reinforced PET-G composites. Four target responses were considered, with three multi-fidelity strategies benchmarked against a standard single-fidelity approach. Across all cases, MFBO achieved higher similarity scores and consistently recovered the targeted responses, outperforming the single-fidelity baseline under the same evaluation budget, while also successfully recovering all targeted responses. These results demonstrate the effectiveness of MFBO for inverse design of stochastic architected materials, where high-quality data is scarce but lower-cost proxies exist. By efficiently navigating complex design spaces, MFBO enables the creation of cellular composites with precisely tailored nonlinear mechanical behaviour.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
