Multi-output physics-informed neural networks for forward and inverse PDE problems with uncertainties

Multi-fidelity Bayesian neural networks: Algorithms and applications

(2021) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport

(2020) QiZhi He et al.   ADVANCES IN WATER RESOURCES

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

(2020) Liu Yang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

hp-VPINNs: Variational physics-informed neural networks with domain decomposition

(2020) Ehsan Kharazmi et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems

(2019) Liang Yan et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

(2019) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A unified deep artificial neural network approach to partial differential equations in complex geometries

(2018) Jens Berg et al.   NEUROCOMPUTING

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems

(2014) Jinglai Li et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING