Forestation projects in southern China over the past few decades have sequestered large amounts of carbon in tree biomass, but the region is approaching saturation of forest carbon storage capacity.
Designated neural network modules are combined to mimic numerically-discretized diffusion-sorption equations, which allows learning “missing pieces” in system understanding and their uncertainties.
A study shows that interactive learning can significantly enhance the performance of artificial intelligence-based parameterization of small-scale processes, a critical component of climate models.
Efficiently tracking nature’s engineers—beavers—at the scale of entire watersheds over time is now possible, thanks to a new artificial intelligence–trained model called EEAGER.
Large data sets can be generated using deep learning to improve the design of observation networks for monitoring subsurface flow and transport.
A new application of machine learning boosts scientists’ ability to use data from satellite navigation systems to detect and warn of earthquakes.
Thanks to the advent of exascale computing, local climate forecasts may soon be a reality. And they’re not just for scientists anymore.
A new special collection invites papers pertaining to the use of machine learning techniques in all sub-fields of heliophysics.
While past attempts to define isotopic endmembers and assign them a geodynamic significance ended in controversy, a machine-learning clustering algorithm offers a solution to this classical problem.