AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation
This work addresses the problem of high computational costs in neural fluid simulations for researchers and engineers, offering an incremental improvement over existing methods.
The paper tackled the challenge of efficiently simulating complex fluid dynamics with long-range dependencies by proposing AMR-Transformer, which integrates adaptive mesh refinement with a constraint-aware pruning module, achieving up to an order-of-magnitude accuracy improvement and a 60x reduction in FLOPs compared to baseline models.
Accurately and efficiently simulating complex fluid dynamics is a challenging task that has traditionally relied on computationally intensive methods. Neural network-based approaches, such as convolutional and graph neural networks, have partially alleviated this burden by enabling efficient local feature extraction. However, they struggle to capture long-range dependencies due to limited receptive fields, and Transformer-based models, while providing global context, incur prohibitive computational costs. To tackle these challenges, we propose AMR-Transformer, an efficient and accurate neural CFD-solving pipeline that integrates a novel adaptive mesh refinement scheme with a Navier-Stokes constraint-aware fast pruning module. This design encourages long-range interactions between simulation cells and facilitates the modeling of global fluid wave patterns, such as turbulence and shockwaves. Experiments show that our approach achieves significant gains in efficiency while preserving critical details, making it suitable for high-resolution physical simulations with long-range dependencies. On CFDBench, PDEBench and a new shockwave dataset, our pipeline demonstrates up to an order-of-magnitude improvement in accuracy over baseline models. Additionally, compared to ViT, our approach achieves a reduction in FLOPs of up to 60 times.