August Johansson

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

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

Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

Physics-informed machine learning

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

The FEniCS Project Version 1.5