基于模拟退火的机器学习驱动三周期极小曲面架构材料优化
Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing
AI总结：
本文利用模拟退火优化随机森林、决策树和XGBoost模型，预测三周期极小曲面结构的拉伸应力，其中SA-XGBoost以R平方0.96表现最佳。
摘要：
本文提出了一种结合机器学习与模拟退火（SA）来优化三周期极小曲面（TPMS）结构拉伸应力的新方法。研究评估了随机森林、决策树和XGBoost模型在预测拉伸应力方面的表现，所用数据集由TPMS模型的有限元分析生成。目标函数通过最小化验证集上的负R平方值来提高模型精度。SA-XGBoost模型表现最优，R平方值达到0.96。相比之下，SA-随机森林模型的R平方值为0.89，而SA-决策树模型在验证分数上波动更大。这表明SA-XGBoost模型在捕捉数据中的复杂关系方面最为有效。模拟退火的引入有助于优化这些机器学习模型的超参数，从而提升其预测能力。
英文摘要：
The research paper presents a novel approach to optimizing the tensile stress of Triply Periodic Minimal Surface (TPMS) structures through machine learning and Simulated Annealing (SA). The study evaluates the performance of Random Forest, Decision Tree, and XGBoost models in predicting tensile stress, using a dataset generated from finite element analysis of TPMS models. The objective function minimized the negative R-squared value on the validation set to enhance model accuracy. The SA-XGBoost model outperformed the others, achieving an R-squared value of 0.96. In contrast, the SA-Random Forest model achieved an R squared value of 0.89 while the SA-Decision Tree model exhibited greater fluctuations in validation scores. This demonstrates that the SA-XGBoost model is most effective in capturing the complex relationships within the data. The integration of SA helps in optimizing the hyperparameters of these machine learning models, thereby enhancing their predictive capabilities.
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。
