Biblio
Filters: Author is Julien Brajard [Clear All Filters]
Learning the hidden dynamics of ocean temperature with neural networks. Proceedings of the 9th International Workshop on Climate informatics: CI 2019 (2019).doi:10.5065/y82j-f154
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear processes in geophysics 26, (2019).
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS) 2, (2020). Abstract
Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting. Remote Sensing 13, (2021).
Classification of sea ice types in sentinel-1 SAR data using convolutional neural networks. Remote Sensing 12, (2020).
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 (2021).doi:10.1098/rsta.2020.0086 Abstract
Connections between data assimilation and machine learning to emulate a numerical model. Proceedings of the 9th International Workshop on Climate informatics: CI 2019 (2019).doi:10.5065/y82j-f154
Impact of sparse profile sampling on the reconstruction of subsurface ocean temperature from surface information. Proceedings of the 9th International Workshop on Climate informatics: CI 2019 (2019).doi:10.5065/y82j-f154
Bridging observations, theory and numerical simulation of the ocean using machine learning. Environmental Research Letters (2021).doi:10.1088/1748-9326/ac0eb0
