[2604.04946] Sparse Autoencoders as a Steering Basis for Phase Synchronization in Graph-Based CFD Surrogates

Computer Science > Computational Engineering, Finance, and Science arXiv:2604.04946 (cs) [Submitted on 28 Mar 2026] Title:Sparse Autoencoders as a Steering Basis for Phase Synchronization in Graph-Based CFD Surrogates Authors:Yeping Hu, Ruben Glatt, Shusen Liu View a PDF of the paper titled Sparse Autoencoders as a Steering Basis for Phase Synchronization in Graph-Based CFD Surrogates, by Yeping Hu and 2 other authors View PDF HTML (experimental) Abstract:Graph-based surrogate models provide fast alternatives to high-fidelity CFD solvers, but their opaque latent spaces and limited controllability restrict use in safety-critical settings. A key failure mode in oscillatory flows is phase drift, where predictions remain qualitatively correct but gradually lose temporal alignment with observations, limiting use in digital twins and closed-loop control. Correcting this through retraining is expensive and impractical during deployment. We ask whether phase drift can instead be corrected post hoc by manipulating the latent space of a frozen surrogate. We propose a phase-steering framework for pretrained graph-based CFD models that combines the right representation with the right intervention mechanism. To obtain disentangled representation for effective steering, we use sparse autoencoders (SAEs) on frozen MeshGraphNet embeddings. To steer dynamics, we move beyond static per-feature interventions such as scaling or clamping, and introduce a temporally coherent, phase-aware method...