AI 中文总结
研究针对航空等领域复杂计算系统，提出贝叶斯优化工具SEGOMOE，利用自适应高斯过程模型处理混合变量，结合专家模型解决非线性问题，支持多保真度数据，经验证在多学科设计中稳健通用。
AI 中文摘要
本研究介绍了SEGOMOE，一种用于优化复杂、计算成本高的系统，特别是航空领域系统的贝叶斯优化工具。它使用自适应高斯过程模型有效处理混合设计变量（连续、离散、分类、分层）。SEGOMOE结合专家模型解决目标和约束中的非线性问题，利用开源代理建模工具箱（SMT）。该工具支持多保真度数据，解决单目标和多目标问题，包括隐藏约束和高维分解。通过基准测试和实际航空应用验证，SEGOMOE在应对多学科挑战方面稳健且通用。
英文摘要
This study introduces SEGOMOE, a Bayesian optimization tool for optimizing complex, computationally expensive systems, especially in aeronautics. It efficiently handles mixed design variables (continuous, discrete, categorical, hierarchical) using adaptive Gaussian process models. SEGOMOE combines expert models to address nonlinearities in objectives and constraints, leveraging the open-source Surrogate Modeling Toolbox (SMT). The tool supports multi-fidelity data and solves both single- and multi-objective problems, including hidden constraints and high-dimensional decomposition. Validated through benchmarks and real-world aeronautical applications, SEGOMOE proves to be robust and versatile for tackling multidisciplinary challenges.
CommentsSEGOMOE. In Proceedings of the 17ème Colloque National en Calcul des Structures (CSMA 2026), Giens, France, May 2026
Journal refBartoli, N., Lefebvre, T., Lafage, R., Saves, P., Diouane, Y., & Morlier, J. (2026). Efficient multidisciplinary design via Bayesian optimization. CSMA 17, Giens, France, May 2026
