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pro vyhledávání: '"Van Leeuwen, Peter Jan"'
A complete and statistically consistent uncertainty quantification for deep learning is provided, including the sources of uncertainty arising from (1) the new input data, (2) the training and testing data (3) the weight vectors of the neural network
Externí odkaz:
http://arxiv.org/abs/2405.20550
Stochastic partial differential equations have been used in a variety of contexts to model the evolution of uncertain dynamical systems. In recent years, their applications to geophysical fluid dynamics has increased massively. For a judicious usage
Externí odkaz:
http://arxiv.org/abs/2305.03548
In a global numerical weather prediction (NWP) modeling framework we study the implementation of Gaussian uncertainty of individual particles into the assimilation step of a localized adaptive particle filter (LAPF). We obtain a local representation
Externí odkaz:
http://arxiv.org/abs/2206.07433
In this work, we use a tempering-based adaptive particle filter to infer from a partially observed stochastic rotating shallow water (SRSW) model which has been derived using the Stochastic Advection by Lie Transport (SALT) approach. The methodology
Externí odkaz:
http://arxiv.org/abs/2112.15216
Measuring time lags between time-series or lighcurves at different wavelengths from a variable or transient source in astronomy is an essential probe of physical mechanisms causing multiwavelength variability. Time-lags are typically quantified using
Externí odkaz:
http://arxiv.org/abs/2106.08623
Publikováno v:
On time-parallel preconditioning for the state formulation of incremental weak constraint 4D-Var. 2021
Using a high degree of parallelism is essential to perform data assimilation efficiently. The state formulation of the incremental weak constraint four-dimensional variational data assimilation method allows parallel calculations in the time dimensio
Externí odkaz:
http://arxiv.org/abs/2105.09802
There is growing awareness that errors in the model equations cannot be ignored in data assimilation methods such as four-dimensional variational assimilation (4D-Var). If allowed for, more information can be extracted from observations, longer time
Externí odkaz:
http://arxiv.org/abs/2101.07249
Many frameworks exist to infer cause and effect relations in complex nonlinear systems but a complete theory is lacking. A new framework is presented that is fully nonlinear, provides a complete information theoretic disentanglement of causal process
Externí odkaz:
http://arxiv.org/abs/2010.02247
Akademický článek
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Model uncertainty estimation using the expectation maximization algorithm and a particle flow filter
Model error covariances play a central role in the performance of data assimilation methods applied to nonlinear state-space models. However, these covariances are largely unknown in most of the applications. A misspecification of the model error cov
Externí odkaz:
http://arxiv.org/abs/1911.01511