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Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs
We present a framework for derivative-based global sensitivity analysis (GSA) for models with high-dimensional input parameters and functional outputs. We combine ideas from derivative-based GSA, random eld representation ...
A distributed active subspace method for scalable surrogate modeling of function valued outputs
Abstract We present a distributed active subspace method for training surrogate models of complex physical processes with high-dimensional inputs and function valued outputs. Specifically, we represent the model output ...