Plain-language summary
High-fidelity CFD simulations can be expensive to run repeatedly. This work investigates a transformer-based surrogate that uses global Fourier information together with localized wavelet information, allowing the model to represent flow behavior occurring at different spatial scales.
Research contribution
- Combines Fourier and wavelet representations for global and local flow structure.
- Uses physics-informed learning to guide the surrogate toward physically meaningful predictions.
- Targets multiscale CFD surrogate modeling for faster simulation-assisted analysis.
Publication status
arXiv preprint arXiv:2606.24696. Submitted to Engineering Applications of Artificial Intelligence.
Code and dataset
Code and dataset links will be added when a public release is available.
Recommended citation
Chakraborty, S., Pan, M., and Chen, X. (2026). A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling. arXiv:2606.24696.
BibTeX
@article{chakraborty2026physics,
title={A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling},
author={Chakraborty, Somyajit and Pan, Ming and Chen, Xizhong},
journal={arXiv preprint arXiv:2606.24696},
year={2026}
}