Physics-Informed Latent Neural Operator for Three-Dimensional Compressor Cascade Flow Prediction
Abstract
A physics-informed latent neural operator framework is proposed for three-dimensional compressor cascade flow prediction. The proposed method combines a Transolver-based latent encoder, which extracts compact global latent representations of flow conditions from point-cloud CFD data, with a coordinate-based PINNs decoder to reconstruct continuous flow fields while incorporating physics-informed residual constraints during training. The results show that the proposed framework can accurately reconstruct pressure and velocity distributions of complex three-dimensional cascade flows and maintains good prediction capability under previously unseen operating conditions. The present method demonstrates promising potential for efficient CFD surrogate modelling and aerodynamic prediction in turbomachinery applications.
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BibTeX
@misc{han2026physicsinformed,
title = {{Physics-Informed Latent Neural Operator for Three-Dimensional Compressor Cascade Flow Prediction}},
author = {Yuling Han and Zhihui Li and Zhibin Yu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.23844},
eprint = {2609.23844},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.23844},
}
RIS
TY - UNPB
TI - Physics-Informed Latent Neural Operator for Three-Dimensional Compressor Cascade Flow Prediction
AU - Yuling Han
AU - Zhihui Li
AU - Zhibin Yu
PY - 2026
DA - 2026/09/20
PB - arXiv
DO - 10.48550/arxiv.2609.23844
UR - https://arxiv.org/abs/2609.23844
AB - A physics-informed latent neural operator framework is proposed for three-dimensional compressor cascade flow prediction. The proposed method combines a Transolver-based latent encoder, which extracts compact global latent representations of flow conditions from point-cloud CFD data, with a coordinate-based PINNs decoder to reconstruct continuous flow fields while incorporating physics-informed residual constraints during training. The results show that the proposed framework can accurately reconstruct pressure and velocity distributions of complex three-dimensional cascade flows and maintains good prediction capability under previously unseen operating conditions. The present method demonstrates promising potential for efficient CFD surrogate modelling and aerodynamic prediction in turbomachinery applications.
ER -