Gappy data: to krig or not to krig?
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Publication:2576310
DOI10.1016/j.jcp.2005.06.023zbMath1216.76062OpenAlexW2123473837MaRDI QIDQ2576310
Sirod Sirisup, Hasan Gunes, George Em. Karniadakis
Publication date: 27 December 2005
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2005.06.023
Related Items (10)
Physics-informed machine learning with conditional Karhunen-Loève expansions ⋮ A data-assimilation method for Reynolds-averaged Navier–Stokes-driven mean flow reconstruction ⋮ Reduced modeling of unknown trajectories ⋮ On the use of kriging for enhanced data reconstruction in a separated transitional flat-plate boundary layer ⋮ Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks ⋮ Gappy spectral proper orthogonal decomposition ⋮ Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning ⋮ Optimal control of the cylinder wake in the laminar regime by trust-region methods and POD reduced-order models ⋮ Sequential estimation of velocity fields using episodic proper orthogonal decomposition ⋮ Predictive flow-field estimation
Uses Software
Cites Work
- Interpolation of spatial data. Some theory for kriging
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Frequency selection and asymptotic states in laminar wakes
- Low-dimensional models for complex geometry flows: Application to grooved channels and circular cylinders
- DPIV-driven flow simulation: a new computational paradigm
- Dynamics and low-dimensionality of a turbulent near wake
- A low-dimensional model for simulating three-dimensional cylinder flow
- Gappy data and reconstruction procedures for flow past a cylinder
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