Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes

no code implementations • 17 Feb 2024 • Jeremiah Hauth, Cosmin Safta, Xun Huan, Ravi G. Patel, Reese E. Jones

In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial-temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant.

Uncertainty Quantification Variational Inference