基于机器学习的双材料热弹性蜂窝超材料逆向设计
Machine learning-enabled inverse design of bimaterial thermoelastic lattice metamaterials
AI总结：
本文提出利用机器学习进行双材料热弹性蜂窝超材料的逆向设计，通过高通量模拟和神经网络模型实现高效预测和优化
摘要：
基于双材料混合蜂窝结构的热弹性超材料，同时表现出负泊松比和负热膨胀系数，非常有希望用于各种应用。本工作致力于利用机器学习（ML）进行此类结构的逆向设计，基于高通量模拟和神经网络模型。通过计算均质化生成具有不同几何特征和基材性质的结构的大数据集。首先训练一个前向ML模型，以高效准确地预测给定结构设计的有效热弹性质。随后开发逆向ML模型，以建议几何特征和基材以实现目标性质。为解决各种逆向设计场景，提出了六种不同的模型，每种模型由不同的目标有效性质和结构设计变量组合定义。训练好的前向模型被整合到逆向模型的损失函数中，并用于生成固定基材情况下的额外数据集。通过代表性设计示例验证了前向和逆向ML模型的良好预测性能。这些ML模型可以应用于高效解决热弹性超材料在新型工程系统中的实际应用中的特定逆向设计任务。
英文摘要：
The thermoelastic metamaterial based on a bimaterial hybrid-honeycomb structure, exhibiting simultaneously negative Poisson's ratios and negative thermal expansion coefficients is very promising for various application. This work is dedicated to the machine learning (ML)-enabled inverse design of such structure, on the basis of high-throughput simulation and neural network models. A large dataset is generated through computational homogenization of structures with varying geometrical features and base material properties. A forward ML model is first trained to efficiently and accurately predict the effective thermoelastic properties for a given structure design. Subsequently, inverse ML models are developed to suggest geometrical features and base materials for desired target properties. To address various inverse design scenarios, six different models are proposed, each defined by different combinations of target effective properties and structural design variables. The trained forward model is integrated into the loss functions of the inverse models and is also employed to generate additional datasets for cases with fixed base materials. The good predictive performance of the forward and inverse ML models is demonstrated by representative design examples. These ML models can be applied to efficiently solving specific inverse design tasks involved in the practical application of the thermoelastic metamaterial in novel engineering systems.
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