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Found 2 papers, 1 papers with code
Sensitivity Analysis of High-Dimensional Models with Correlated Inputs
Sensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or study the uncertainty propagation through the input-output interactions.
Resampling with neural networks for stochastic parameterization in multiscale systems
This method is used to formulate a parameterization that is stochastic, taking the uncertainty of the unresolved scales into account.
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