![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.
Generative machine learning models, including GANs (Generative Adversarial Networks), are a powerful tool toward searching chemical space for desired functionalities.
A numerical weather forecasting model (WRF) was used to simulate 120 storms over the Alabama-Coosa-Tallapoosa (ACT) river basin to explore the effect of climate change on probable maximum precipitation (PMP).