Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220781192> ?p ?o ?g. }
Showing items 1 to 58 of
58
with 100 items per page.
- W4220781192 abstract "<p>Recent years have seen active efforts within the geophysical community to combine traditional Data Assimilation (DA) methods with emerging Machine Learning (ML) techniques. However, most of this past theoretical work has been centered on variational DA approaches due to their similarity with ML in terms of how the underlying optimization problem is formulated and solved. Here I will present a new and completely general nonlinear estimation theory that retains the flexibility of advanced sampling-based methods (e.g., the particle filter) and the analytical tractability of linear estimation algorithms (e.g., the ensemble Kalman filter). In particular, an alternative state space model will be constructed whose filtering and smoothing distributions remain closed under a wide class of nonlinear functions. Since these nonlinear functions are only required to be bijective and continuously differentiable, the new estimation theory serves an ideal framework for rigorously incorporating invertible neural networks in the DA design. There are two additional properties which make the proposed framework especially appealing. First, linear estimation results follow immediately upon substituting the invertible neural networks with the identity transformation. Second, the prior and posterior belong to the same distribution family, which implies that the correlation structure and the corresponding dynamical balances in the model state are preserved following the analysis step. During the upcoming EGU meeting, I will discuss the motivation behind the new estimation framework, place it in the context of existing nonlinear DA techniques and demonstrate some of its benefits through idealized numerical examples.</p>" @default.
- W4220781192 created "2022-04-03" @default.
- W4220781192 creator A5001694042 @default.
- W4220781192 date "2022-03-27" @default.
- W4220781192 modified "2023-10-17" @default.
- W4220781192 title "Bridging linear state estimation and machine learning" @default.
- W4220781192 doi "https://doi.org/10.5194/egusphere-egu22-2838" @default.
- W4220781192 hasPublicationYear "2022" @default.
- W4220781192 type Work @default.
- W4220781192 citedByCount "0" @default.
- W4220781192 crossrefType "posted-content" @default.
- W4220781192 hasAuthorship W4220781192A5001694042 @default.
- W4220781192 hasConcept C11413529 @default.
- W4220781192 hasConcept C121332964 @default.
- W4220781192 hasConcept C126255220 @default.
- W4220781192 hasConcept C154945302 @default.
- W4220781192 hasConcept C157286648 @default.
- W4220781192 hasConcept C158622935 @default.
- W4220781192 hasConcept C202444582 @default.
- W4220781192 hasConcept C28826006 @default.
- W4220781192 hasConcept C31972630 @default.
- W4220781192 hasConcept C33923547 @default.
- W4220781192 hasConcept C3770464 @default.
- W4220781192 hasConcept C41008148 @default.
- W4220781192 hasConcept C50644808 @default.
- W4220781192 hasConcept C62520636 @default.
- W4220781192 hasConcept C96442724 @default.
- W4220781192 hasConceptScore W4220781192C11413529 @default.
- W4220781192 hasConceptScore W4220781192C121332964 @default.
- W4220781192 hasConceptScore W4220781192C126255220 @default.
- W4220781192 hasConceptScore W4220781192C154945302 @default.
- W4220781192 hasConceptScore W4220781192C157286648 @default.
- W4220781192 hasConceptScore W4220781192C158622935 @default.
- W4220781192 hasConceptScore W4220781192C202444582 @default.
- W4220781192 hasConceptScore W4220781192C28826006 @default.
- W4220781192 hasConceptScore W4220781192C31972630 @default.
- W4220781192 hasConceptScore W4220781192C33923547 @default.
- W4220781192 hasConceptScore W4220781192C3770464 @default.
- W4220781192 hasConceptScore W4220781192C41008148 @default.
- W4220781192 hasConceptScore W4220781192C50644808 @default.
- W4220781192 hasConceptScore W4220781192C62520636 @default.
- W4220781192 hasConceptScore W4220781192C96442724 @default.
- W4220781192 hasLocation W42207811921 @default.
- W4220781192 hasOpenAccess W4220781192 @default.
- W4220781192 hasPrimaryLocation W42207811921 @default.
- W4220781192 hasRelatedWork W1256201854 @default.
- W4220781192 hasRelatedWork W1604874922 @default.
- W4220781192 hasRelatedWork W1680744822 @default.
- W4220781192 hasRelatedWork W2035444983 @default.
- W4220781192 hasRelatedWork W2107970527 @default.
- W4220781192 hasRelatedWork W2184095694 @default.
- W4220781192 hasRelatedWork W2352531415 @default.
- W4220781192 hasRelatedWork W2352549790 @default.
- W4220781192 hasRelatedWork W2725647457 @default.
- W4220781192 hasRelatedWork W337648361 @default.
- W4220781192 isParatext "false" @default.
- W4220781192 isRetracted "false" @default.
- W4220781192 workType "article" @default.