Abstract:

To repair noisy or partially missing fluid flow data and achieve accurate fluid dynamics
analysis, a physics-informed variational autoencoder model is proposed. First, the variational autoencoder is employed to learn the latent representation of fluid flow. Second, spatiotemporal coordinate information is combined with this latent representation, and the partial derivatives of the decoded flow field information with respect to the spatiotemporal coordinates are obtained by automatic differentiation technology. Finally, physical prior information of fluid dynamics is introduced to construct a physical constraint loss term. This ensures that the generated data conforms to both the key features of the flow and the underlying physical laws, thereby enhancing the physical consistency and reconstruction accuracy while providing a certain level of interpretability. Experimental results demonstrate that the proposed model achieves higher accuracy than existing methods in dealing with flow field noise and data missing issues. Its effectiveness is also proven in both two-dimensional and three-dimensional complex vortex flow fields.

Key words: artificial intelligence,  computational fluid dynamics, physical priors, data denoising, flow field reconstruction