Ensemble Kalman filters and geometric characterization of sensitivity spaces for uncertainty quantification in optimization

DOI10.1016/j.cma.2015.03.006zbMath1423.90163OpenAlexW2033665966MaRDI QIDQ1734469

Bijan Mohammadi

Publication date: 27 March 2019

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2015.03.006

zbMATH Keywords

inverse problemsuncertainty quantificationhistory matchingextreme scenariosprincipal anglesEnKF

Mathematics Subject Classification ID

Sensitivity, stability, parametric optimization (90C31) Stochastic programming (90C15) Optimization of other properties in solid mechanics (74P10)

Related Items (2)

Controlling first four moments for robust optimization ⋮ Uncertainty quantification in littoral erosion

Uses Software

• EnKF

Cites Work

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• Robust optimization of dense gas flows under uncertain operating conditions
• Efficient shape optimization for certain and uncertain aerodynamic design
• Uncertainty based robust optimization method for drag minimization problems in aerodynamics
• Discontinuous upwinding and mixed finite elements for two-phase flows in reservoir simulation
• Model order reduction: Theory, research aspects and applications. Selected papers based on the presentations at the workshop `Model order reduction, coupled problems and optimization', Leiden, The Netherlands, September 19--23, 2005.
• Great circle fibrations of the three-sphere
• Uncertainty quantification by geometric characterization of sensitivity spaces
• Angles between Euclidean subspaces
• On the construction and analysis of stochastic models: characterization and propagation of the errors associated with limited data
• Stochastic processes and filtering theory
• Multi-Element Generalized Polynomial Chaos for Arbitrary Probability Measures
• Modeling Uncertainty in the Earth Sciences
• Certified real‐time solution of the parametrized steady incompressible Navier–Stokes equations: rigorous reduced‐basis a posteriori error bounds
• Reduced sampling and incomplete sensitivity for low‐complexity robust parametric optimization
• Sparse grids
• S<scp>HAPE</scp> O<scp>PTIMIZATION IN</scp> F<scp>LUID</scp> M<scp>ECHANICS</scp>

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