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Found 6 papers, 1 papers with code
Transmission matrix parameter estimation of COVID-19 evolution with age compartments using ensemble-based data assimilation
The COVID-19 pandemic and its multiple outbreaks have challenged governments around the world.
Online machine-learning forecast uncertainty estimation for sequential data assimilation
The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman-like analysis update and the machine learning based estimation of a state-dependent forecast error covariance matrix.
Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification
Experiments using the Lorenz'96 model show that the ANNs are able to emulate some of the properties of ensemble forecasts like the filtering of the most unpredictable modes and a state-dependent quantification of the forecast uncertainty.
A Framework for Causal Discovery in non-intervenable systems
Many frameworks exist to infer cause and effect relations in complex nonlinear systems but a complete theory is lacking.
Kernel embedded nonlinear observational mappings in the variational mapping particle filter
In this work, we evaluate nonlinear observational mappings in the variational mapping method using two approximations that avoid the adjoint, an ensemble based approximation in which the gradient is approximated by the particle covariances in the state and observational spaces the so-called ensemble space and an RKHS approximation in which the observational mapping is embedded in an RKHS and the gradient is derived there.
Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter
In this work, a novel sequential Monte Carlo filter is introduced which aims at efficient sampling of high-dimensional state spaces with a limited number of particles.
