This paper introduces a methodology designed to augment the inverse design optimization process in scenarios constrained by limited compute, through the strategic synergy of multi-fidelity evaluations, machine learning models, and optimization algorithms. The proposed methodology is analyzed on two distinct engineering inverse design problems: airfoil inverse design and the scalar field reconstruction problem. It leverages a machine learning model trained with low-fidelity simulation data, in each optimization cycle, thereby proficiently predicting a target variable and discerning whether a high-fidelity simulation is necessitated, which notably conserves computational resources. Additionally, the machine learning model is strategically deployed prior to optimization to reduce the search space, thereby further accelerating convergence toward the optimal solution. The methodology has been employed to enhance two optimization algorithms, namely Differential Evolution and Particle Swarm Optimization. Comparative analyses illustrate performance improvements across both algorithms. Notably, this method is adeptly adaptable across any inverse design application, facilitating a harmonious synergy between a representative low-fidelity machine learning model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.

翻译：本文提出了一种在计算资源受限场景下，通过多保真度评估、机器学习模型与优化算法的战略协同来增强逆向设计优化流程的方法。该方法通过两个不同的工程逆向设计问题（翼型逆向设计与标量场重建问题）进行了分析。在每个优化周期中，该方法利用基于低保真度仿真数据训练的机器学习模型，高效预测目标变量，并判断是否需要高保真度仿真，从而显著节省计算资源。此外，该机器学习模型在优化过程之前被策略性地部署以缩减搜索空间，进一步加速向最优解的收敛。该方法被应用于增强两种优化算法（即差分进化算法与粒子群优化算法）。对比分析表明，两种算法的性能均得到提升。值得注意的是，该方法可灵活适配任何逆向设计应用，促进代表性低保真度机器学习模型与高保真度仿真之间的和谐协同，并能够无缝集成至任何基于种群的优化算法中。