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.

该研究介绍了一种方法，通过多精度评估、机器学习模型和优化算法的战略性协同，增强受有限计算资源约束的反向设计优化过程。该方法在两个不同的工程反向设计问题上进行了分析，通过在每个优化循环中利用训练有低精度模拟数据的机器学习模型，高效地预测目标变量并确定是否需要高精度模拟，从而显著节省了计算资源。此外，该机器学习模型在优化之前被战略性地使用以减小搜索空间，进一步加快了收敛到最优解的速度。该方法已被应用于增强差分进化和粒子群优化两种优化算法，对比分析表明两种算法的性能有所提升。值得注意的是，该方法适用于任何反向设计应用，实现了低精度机器学习模型和高精度模拟之间的和谐协同，并且可以无缝应用于任何种类的基于群体的优化算法。