Interpreting CFD Surrogates through Sparse Autoencoders

• URL: http://arxiv.org/abs/2507.16069v1
• Date: Mon, 21 Jul 2025 21:09:45 GMT
• Title: Interpreting CFD Surrogates through Sparse Autoencoders
• Authors: Yeping Hu, Shusen Liu,
• Abstract summary: This work introduces a posthoc interpretability framework for graph-based surrogate models used in computational fluid dynamics (CFD)<n>By obtaining an overcomplete basis in the node embedding space of a pretrained surrogate, the method extracts a dictionary of interpretable latent features.<n>The approach enables the identification of monosemantic concepts aligned with physical phenomena such as vorticity or flow structures.
• Score: 6.842974489069953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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