Cross-Domain Pretraining for Steady-State Neural CFD Surrogates

2026-10-07

AI Summary

This study addresses the limited cross-geometry and cross-scenario generalization of neural CFD surrogate models and the prohibitive cost of generating new training data. We propose a few-shot efficient fine-tuning method based on cross-domain pre-training and multi-source data pooling. Our findings demonstrate that simply pooling diverse steady-state datasets significantly enhances model generalization, an advantage that proves architecture-agnostic and scales with both model size and data diversity. Experimental results show that, compared to training from scratch, the proposed approach reduces prediction error by 2–3× under identical sample budgets, or achieves equivalent accuracy with 8× fewer samples. This work establishes an efficient transfer learning paradigm for CFD surrogate modeling.

Abstract

Neural surrogates for computational fluid dynamics (CFD) have the potential to greatly enhance engineering innovation through accelerating simulation. However, the primary limitation for neural surrogates is the lack of generalization to geometries and applications beyond the training set, which is significant given the diversity of engineering scenarios. Currently, this is addressed by generating a new dataset for a specific application; however, this requires running costly numerical solvers. In this work, we take a step toward addressing this by studying neural surrogates trained across different geometries, boundary conditions, and fidelities. We find that cross-domain pretraining improves zero- and few-shot performance on held-out datasets relative to both training from scratch and transferring from domain-specific experts. In particular, finetuning a pretrained, cross-domain model can achieve 2-3x lower errors at the same sample size and use 8x fewer samples to achieve the same error, compared to training from scratch. This benefit is architecture agnostic and improves with model size and pretraining dataset diversity. Furthermore, we study how and why cross-domain pretraining works in CFD surrogates, and find that simply pooling steady-state datasets is both sufficient and effective. Given the high cost of generating CFD data, leveraging existing datasets through cross-domain pretraining will likely be a valuable strategy as future surrogates expand to tackle new problems and use cases.

Problem

• Computational Fluid Dynamics
• Neural Surrogates
• Cross-Domain Pretraining
• Generalization
• Data Efficiency

Innovation

• Cross-Domain Pretraining
• Neural CFD Surrogates
• Few-Shot Generalization
• Steady-State Simulation
• Transfer Learning