$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
This addresses the problem of uncertainty quantification in physics-informed neural networks for researchers in computational science, though it appears incremental as it builds on existing domain decomposition techniques.
The paper tackles the challenge of efficiently quantifying uncertainties in multi-scale PDEs by proposing $PINN, a domain decomposition method for Bayesian Physics-Informed Neural Networks, which recovers global uncertainty more efficiently by computing local uncertainties concurrently, as demonstrated with up to 15% noise in training data.
Physics-Informed Neural Networks (PINNs) are a novel computational approach for solving partial differential equations (PDEs) with noisy and sparse initial and boundary data. Although, efficient quantification of epistemic and aleatoric uncertainties in big multi-scale problems remains challenging. We propose \$PINN a novel method of computing global uncertainty in PDEs using a Bayesian framework, by combining local Bayesian Physics-Informed Neural Networks (BPINN) with domain decomposition. The solution continuity across subdomains is obtained by imposing the flux continuity across the interface of neighboring subdomains. To demonstrate the effectiveness of \$PINN, we conduct a series of computational experiments on PDEs in 1D and 2D spatial domains. Although we have adopted conservative PINNs (cPINNs), the method can be seamlessly extended to other domain decomposition techniques. The results infer that the proposed method recovers the global uncertainty by computing the local uncertainty exactly more efficiently as the uncertainty in each subdomain can be computed concurrently. The robustness of \$PINN is verified by adding uncorrelated random noise to the training data up to 15% and testing for different domain sizes.