Biblio
Filters: Author is Julien Brajard [Clear All Filters]
Bridging observations, theory and numerical simulation of the ocean using machine learning. Environmental Research Letters 16, (2021).
Twenty-One Years of Phytoplankton Bloom Phenology in the Barents, Norwegian, and North Seas. Frontiers in Marine Science 8, (2021).
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model. Journal of Computational Science 44, (2020).
Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, (2021). Abstract
Classification of sea ice types in sentinel-1 SAR data using convolutional neural networks. Remote Sensing 12, (2020).
Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting. Remote Sensing 13, (2021).
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS) 2, (2020). Abstract
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear processes in geophysics 26, (2019).
Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods. Ocean Science 18, (2022).
54 years of microboring community history explored by machine learning in a massive coral from Mayotte (Indian Ocean). Frontiers in Marine Science 9, (2022).
