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Found 11 papers, 8 papers with code
SPDE priors for uncertainty quantification of end-to-end neural data assimilation schemes
Recent advances in the deep learning community also enables to adress this problem as neural architecture embedding data assimilation variational framework.
OceanBench: The Sea Surface Height Edition
It provides plug-and-play data and pre-configured pipelines for ML researchers to benchmark their models and a transparent configurable framework for researchers to customize and extend the pipeline for their tasks.
Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
Optimal Interpolation (OI) is a widely used, highly trusted algorithm for interpolation and reconstruction problems in geosciences.
Orthonormal Convolutions for the Rotation Based Iterative Gaussianization
Here we present the \emph{Convolutional RBIG}: an extension that alleviates this issue by imposing that the rotation in RBIG is a convolution.
The Kernelized Taylor Diagram
Our proposed kernelized Taylor diagram is capable of visualizing similarities between populations with minimal assumptions of the data distributions.
Information Theory in Density Destructors
Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy).
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks
We harness the power of deep learning and successfully apply Convolutional Neural Networks to regress stellar rotation periods from Kepler light curves.
Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable.
Information Theory Measures via Multidimensional Gaussianization
Information theory is an outstanding framework to measure uncertainty, dependence and relevance in data and systems.
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences
We note that model function derivatives in kernel machines is proportional to the kernel function derivative.
Accounting for Input Noise in Gaussian Process Parameter Retrieval
In this letter, we demonstrate how one can account for input noise estimates using a GP model formulation which propagates the error terms using the derivative of the predictive mean function.
