A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling

Physics-informed Fourier-wavelet transformer improves multiscale CFD surrogate modeling

A new arXiv preprint introduces a physics-informed Fourier-wavelet transformer for next-step velocity-field reconstruction in computational fluid dynamics, combining hybrid spectral encoding with PDE-residual-guided self-attention and self-supervised pretraining. The model is evaluated on cylinder-wake and fluid-structure interaction benchmarks, achieving best-in-class normalized mean-squared error on both tasks and stronger recovery of localized flow structures compared to spectral, transformer, and physics-informed neural network baselines. The work targets the persistent gap between global flow pattern accuracy and fine-grained multiscale structure recovery in surrogate models.