![Parameter posterior distributions estimated by INN and Markov Chain Monte Carlo (MCMC). The INN produces similar posteriors with the MCMC sampling but 30 times faster. CSED Computational Sciences and Engineering ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-07/invertible_neural_networks_for_earth_system_model_calibration_and_simulation.png?h=7370987d&itok=jgmDkY2A)
Oak Ridge National Laboratory researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
Oak Ridge National Laboratory researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models.