Statistical finite elements for misspecified models

DOI10.1073/pnas.2015006118zbMath1485.65104OpenAlexW3116299394WikidataQ114102171 ScholiaQ114102171MaRDI QIDQ5073255

Connor Duffin, Mark A. Girolami, Edward Cripps

Publication date: 5 May 2022

Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1073/pnas.2015006118

Mathematics Subject Classification ID

Bayesian inference (62F15) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60)

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Cites Work

• Unnamed Item
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• Lessons in uncertainty quantification for turbulent dynamical systems
• Bayesian inference for differential equations
• Numerical simulation of the stochastic Korteweg-de Vries equation
• Indistinguishable states. II: The imperfect model scenario
• The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions
• Damping of large-amplitude solitary waves
• Uncertainty estimation and prediction for interdisciplinary ocean dynamics
• Bayesian Calibration of Computer Models
• Inverse problems: A Bayesian perspective
• Time Series Analysis and Its Applications
• A weakly nonlinear model of long internal waves in closed basins
• Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models
• Data Assimilation
• Sediment Resuspension and Transport by Internal Solitary Waves
• LONG NONLINEAR INTERNAL WAVES
• Stochastic differential equations. An introduction with applications.