A multiscale framework for probability measures in Wasserstein spaces is developed, including a refinement operator preserving geodesic structure and an optimality number for detecting non-geodesic dynamics across scales.
American Mathematical Soc., 2021
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A reduced-order model for parametrized optimal transport problems using low-dimensional cone or subspace constraints and EIM-based error estimation.
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Multiscaling in Wasserstein Spaces
A multiscale framework for probability measures in Wasserstein spaces is developed, including a refinement operator preserving geodesic structure and an optimality number for detecting non-geodesic dynamics across scales.
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A reduced-order model for parametrized Optimal Transport problems
A reduced-order model for parametrized optimal transport problems using low-dimensional cone or subspace constraints and EIM-based error estimation.