<ul><li>Variational inference (VI) is a computationally efficient and scalable methodology for approximate Bayesian inference, excelling at generative modelling and inversion tasks.</li><li>This paper provides an accessible and thorough technical introduction to VI for physics-related problems, explaining the standard derivations of the VI framework and its realization through deep learning.</li><li>It highlights the importance of the underlying physical model in capturing the dynamics of interest and offers flexibility in uncertainty quantification.</li><li>The target audience of this paper is the scientific community focusing on physics-based problems and uncertainty quantification.</li></ul>

A Primer on Variational Inference for Physics-Informed Deep Generative Modelling

Discover more