Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems
The authors developed a Variational Graph Neural Network (VGNN) to model weight probability distributions, enabling efficient uncertainty quantification in inverse problems. They integrated variational layers into the decoder to estimate both cognitive and statistical uncertainty at a lower computational cost than full Bayesian methods. Validation showed the model accurately recovered physical parameters and provided physically consistent confidence intervals.