用稀疏自编码器解析流体模拟模型的隐藏特征，让黑箱变透明。
Interpreting CFD Surrogates through Sparse Autoencoders
- 通过稀疏自编码器在节点嵌入空间中提取可解释的特征字典
- 识别出与涡度、流动结构等物理现象对齐的单一语义概念
- 适用于需要高可信度的流体仿真场景，如航空航天设计
基于学习的代理模型已成为高保真计算流体动力学（CFD）求解器的实用替代方案，但其隐含表示仍不透明，限制了在安全关键或受监管环境中的应用。本文提出一种后处理可解释性框架，针对图结构代理模型，利用稀疏自编码器（SAEs）在预训练代理模型的节点嵌入空间中获取过完备基，从而提取一组可解释的潜在特征。该方法能够识别出与涡度、流动结构等物理现象对齐的单语义概念，为CFD应用提供了一条模型无关的可解释性增强路径，提升模型的可信度与可接受性。
原文摘要 · Abstract (English)
Learning-based surrogate models have become a practical alternative to high-fidelity CFD solvers, but their latent representations remain opaque and hinder adoption in safety-critical or regulation-bound settings. This work introduces a posthoc interpretability framework for graph-based surrogate models used in computational fluid dynamics (CFD) by leveraging sparse autoencoders (SAEs). By obtaining an overcomplete basis in the node embedding space of a pretrained surrogate, the method extracts a dictionary of interpretable latent features. The approach enables the identification of monosemantic concepts aligned with physical phenomena such as vorticity or flow structures, offering a model-agnostic pathway to enhance explainability and trustworthiness in CFD applications.
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