Uncertainty-Aware Flow Field Reconstruction with SVGP-Based Neural Networks

This research explores a novel approach to flow field reconstruction using a combination of Stochastic Variational Gaussian Processes (SVGP) and Kolmogorov-Arnold Networks, incorporating uncertainty estimation. The paper's contribution lies in its application of SVGP within a specific neural network architecture for improved accuracy and reliability in fluid dynamics simulations.

• •Applies SVGP within Kolmogorov-Arnold Networks for flow field reconstruction.
• •Focuses on uncertainty quantification in the reconstruction process.
• •Potentially improves accuracy and robustness in fluid dynamics modeling.