通过多保真模拟、机器学习和边界细化策略实现高效的反向设计优化

IF 8.7 2区 工程技术 Q1 Mathematics
Luka Grbcic, Juliane Müller, Wibe Albert de Jong
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引用次数: 0

摘要

本文介绍了一种方法,旨在通过多保真度评估、机器学习模型和优化算法的战略协同作用,在计算能力有限的情况下增强反设计优化过程。本文针对两个不同的工程逆向设计问题:机翼逆向设计和标量场重建问题,对所提出的方法进行了分析。该方法在每个优化周期中利用低保真仿真数据训练的机器学习模型,从而熟练预测目标变量并判断是否有必要进行高保真仿真,这显著节省了计算资源。此外,机器学习模型会在优化之前进行战略性部署,以压缩设计空间边界,从而进一步加快向最优解的收敛。该方法被用于增强两种优化算法,即差分进化和粒子群优化。对比分析表明,这两种算法的性能都有所提高。值得注意的是,这种方法适用于任何逆向设计应用,促进了代表性低保真 ML 模型与高保真仿真之间的协同作用,并可无缝应用于各种基于种群的优化算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies

Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies

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 compress the design space boundaries, 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 adaptable across any inverse design application, facilitating a synergy between a representative low-fidelity ML model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.

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来源期刊
Engineering with Computers
Engineering with Computers 工程技术-工程:机械
CiteScore
16.50
自引率
2.30%
发文量
203
审稿时长
9 months
期刊介绍: Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.
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