Boosting kernel-based dimension reduction for jointly propagating spatial variability and parameter uncertainty in long-running flow simulators
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Publication:887633
DOI10.1007/s11004-014-9551-0zbMath1323.86033OpenAlexW2089980523MaRDI QIDQ887633
Publication date: 26 October 2015
Published in: Mathematical Geosciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11004-014-9551-0
variable selectionhigh-dimensionbasis set expansioncomponent-wise gradient boostingcomputationally intensive flow modelspatially dependent input
Uses Software
Cites Work
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- Greedy function approximation: A gradient boosting machine.
- Boosting algorithms: regularization, prediction and model fitting
- Kernel principal component analysis for stochastic input model generation
- Kernel principal component analysis for efficient, differentiable parametrization of multipoint geostatistics
- Representing spatial uncertainty using distances and kernels
- An efficient, high-order perturbation approach for flow in random porous media via Karhunen-Loève and polynomial expansions.
- Principal component analysis.
- Conditional simulation of complex geological structures using multiple-point statistics
- Boosting for high-dimensional linear models
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
