用双自编码器实现参数化设计实时生成与多参数解逼近
Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximation
- 双秩降低自编码器分别处理几何与解空间，通过潜在空间回归关联
- 在两相微结构上验证，支持两个关键材料参数的多参数解计算
- 适合需要快速设计探索与实时仿真优化的工程场景
本文提出一种新型仿真驱动工程科学范式——生成式参数化设计（GPD）框架。该框架可在给定参数条件下实时生成新设计及其对应的简化广义分解（sPGD）解基。通过两个秩降低自编码器（RRAE）分别编码并生成几何形态与稀疏sPGD模式解，二者在潜在空间中通过回归技术耦合，实现设计与解之间的高效转换。该框架可显著提升设计探索与优化效率，推动数字孪生与混合孪生系统发展，增强工程应用中的预测建模与实时决策能力。所提方法在两相微结构案例中得到验证，其多参数解能有效捕捉两个关键材料参数的变化影响。
原文摘要 · Abstract (English)
This paper presents a novel paradigm in simulation-based engineering sciences by introducing a new framework called Generative Parametric Design (GPD). The GPD framework enables the generation of new designs along with their corresponding parametric solutions given as a reduced basis. To achieve this, two Rank Reduction Autoencoders (RRAEs) are employed, one for encoding and generating the design or geometry, and the other for encoding the sparse Proper Generalized Decomposition (sPGD) mode solutions. These models are linked in the latent space using regression techniques, allowing efficient transitions between design and their associated sPGD modes. By empowering design exploration and optimization, this framework also advances digital and hybrid twin development, enhancing predictive modeling and real-time decision-making in engineering applications. The developed framework is demonstrated on two-phase microstructures, in which the multiparametric solutions account for variations in two key material parameters.
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