用强化学习优化汽车侧围结构，提升碰撞安全性能。
Developement of Reinforcement Learning based Optimisation Method for Side-Sill Design
- 基于强化学习构建逆向多参数多目标优化框架
- 结合有限元求解器实现复杂结构的高效设计迭代
- 适合汽车安全设计与智能优化领域研究者参考
车辆碰撞安全性优化是汽车研发中的关键环节。面对严格法规和市场对高安全性的需求，必须在有限时间内综合考虑多种因素。对于最优碰撞安全设计，需进行多目标优化，而复杂部件则涉及多个设计参数的评估。此类碰撞分析依赖计算量巨大的有限元模拟，因而亟需逆向多参数多目标优化方法。本文研究一种基于机器学习的优化方法，聚焦于多腔体侧围结构的优化设计，以提升碰撞安全性能。同时，将优化器与有限元求解器耦合，实现了更优的设计结果。
原文摘要 · Abstract (English)
Optimisation for crashworthiness is a critical part of the vehicle development process. Due to stringent regulations and increasing market demands, multiple factors must be considered within a limited timeframe. However, for optimal crashworthiness design, multiobjective optimisation is necessary, and for complex parts, multiple design parameters must be evaluated. This crashworthiness analysis requires computationally intensive finite element simulations. This challenge leads to the need for inverse multi-parameter multi-objective optimisation. This challenge leads to the need for multi-parameter, multi-objective inverse optimisation. This article investigates a machine learning-based method for this type of optimisation, focusing on the design optimisation of a multi-cell side sill to improve crashworthiness results. Furthermore, the optimiser is coupled with an FE solver to achieve improved results.
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