By Zhuo Zhang, Shun Zou, Canqun Yang, Xi YangSource:
[arXiv cs.LG](https://arxiv.org/list/cs.LG/recent)
arXiv:2609.29087v1 Announce Type: new
Abstract: While
deep learningaccelerates expensive partial differential equation solving in computational fluid dynamics (CFD), existing methods like PINNs and FNOs often struggle with generalization, noise robustness, and physical consistency. We introduce theTransformer-Mamba for Flow Field (TM4FF) framework, a physics-constrained operator learning model with three key innovations: a Residual Wavelet Mamba (RWM) layer for feature denoising, a Transformer-basedattention mechanismfor enhanced feature fusion, and a physics-informed loss using Fourier derivatives to enforce the Navier-Stokes equations. Experiments on four CFD datasets show TM4FF achieves high accuracy and robust generalization across varying flow conditions. Get AI news in your inbox
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Key Terms Explained #
Attention
A mechanism that lets neural networks focus on the most relevant parts of their input when producing output.
Attention Mechanism
The attention mechanism is a technique that lets neural networks focus on the most relevant parts of their input when producing output.
Deep Learning
A subset of machine learning that uses neural networks with many layers (hence 'deep') to learn complex patterns from large amounts of data.
Transformer
The neural network architecture behind virtually all modern AI language models.