Ensemble Kalman filter for nonconservative moving mesh solvers with a joint physics and mesh location update
Numerical solvers using adaptive meshes can focus computational power on important
regions of a model domain capturing important or unresolved physics. The adaptation can …
regions of a model domain capturing important or unresolved physics. The adaptation can …
Data assimilation using adaptive, non-conservative, moving mesh models
Numerical models solved on adaptive moving meshes have become increasingly prevalent
in recent years. Motivating problems include the study of fluids in a Lagrangian frame and …
in recent years. Motivating problems include the study of fluids in a Lagrangian frame and …
The convergence of stochastic differential equations to their linearisation in small noise limits
Prediction via deterministic continuous-time models will always be subject to model error, for
example due to unexplainable phenomena, uncertainties in any data driving the model, or …
example due to unexplainable phenomena, uncertainties in any data driving the model, or …
Rigorous Convergence Bounds for Stochastic Differential Equations with Application to Uncertainty Quantification
Prediction via continuous-time models will always be subject to model error, for example
due to unexplainable phenomena, uncertainties in any data driving the model, or …
due to unexplainable phenomena, uncertainties in any data driving the model, or …
Methods of Ensemble Data Assimilation on Adaptive Moving Meshes
C Guider - 2019 - search.proquest.com
Numerical models solved on adaptive moving meshes have become increasingly prevalent
in recent years. In particular, neXtSIM is a 2D model of sea-ice that is numerically solved on …
in recent years. In particular, neXtSIM is a 2D model of sea-ice that is numerically solved on …
[CITAZIONE][C] Computable Characterisations of Uncertainty in Differential Equations
LAA Blake - 2024