Active Subspaces - Emerging Ideas for Dimension Reduction in Parameter Studies

By Paul G. Constantine. SIAM spotlights, 2015

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The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “SA is now considered a requirement for good modeling practice as indicated by some existing guidelines (European Commission, 2015; Saltelli et al., 2020).”
Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (2019, background): “In fact, if one considers the task of approximating a generic nonlinear, multivariate function with scalar response, the computational time needed for performing sufficient forward model evaluations quickly becomes unfeasible even for inexpensive computer codes [27].”
Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media (2017, background): “imate suciently well the sought input-output map, they exhibit signicant limitations in high input dimensions (e.g. in the hundreds), an instantiation of the well-documented curse of dimensionality [15]. One could argue that employing larger, more exible emulators, e.g. as those arising in the conte”