Biblio
Filters: Author is Julien Brajard [Clear All Filters]
Learning the hidden dynamics of ocean temperature with neural networks. (2019).doi:10.5065/y82j-f154
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).
Classification of sea ice types in sentinel-1 SAR data using convolutional neural networks. Remote Sensing 12, (2020).
Connections between data assimilation and machine learning to emulate a numerical model. (2019).doi:10.5065/y82j-f154
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).
Impact of sparse profile sampling on the reconstruction of subsurface ocean temperature from surface information. (2019).doi:10.5065/y82j-f154
