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Subspace-based dimension reduction for forward and inverse uncertainty quantification

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UNQW04 - UQ for inverse problems in complex systems

Many methods in uncertainty quantification suffer from the curse of dimensionality. I will discuss several approaches for identifying exploitable low-dimensional structure—e.g., active subspaces or likelihood-informed subspaces—-that enable otherwise infeasible forward and inverse uncertainty quantification.

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