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arXiv Machine Learning By Zhuo Zhang, Shun Zou, Canqun Yang, Xi Yang

Physics and Data Driven Transformer-Mamba Framework for Flow Field

Read the original on arXiv Machine Learning →

The paper introduces the Transformer-Mamba for Flow Field (TM4FF), a physics-constrained operator learning model designed to improve partial differential equation solving in computational fluid dynamics. TM4FF incorporates a Residual Wavelet Mamba layer for feature denoising, a Transformer-based attention mechanism for enhanced feature fusion, and a physics-informed loss that enforces the Navier-Stokes equations using Fourier derivatives. Experiments on four CFD datasets demonstrate that TM4FF achieves high accuracy and robust generalization across varying flow conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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