A physics-informed Fourier-wavelet transformer model reports the lowest normalized mean-squared error on cylinder-wake and fluid-structure interaction velocity-field benchmarks compared with spectral, transformer, operator-learning, and PINN baselines.
org/CorpusID:244714159
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Low-Rank Spatial Attention unifies global mixing in neural operators with standard Transformer components and reduces error by over 17%.
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A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling
A physics-informed Fourier-wavelet transformer model reports the lowest normalized mean-squared error on cylinder-wake and fluid-structure interaction velocity-field benchmarks compared with spectral, transformer, operator-learning, and PINN baselines.
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Simple yet Effective: Low-Rank Spatial Attention for Neural Operators
Low-Rank Spatial Attention unifies global mixing in neural operators with standard Transformer components and reduces error by over 17%.