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Transformed snapshot interpolation

Functions with jumps and kinks typically arising from parameter dependent or stochastic hyperbolic PDEs are notoriously difficult to approximate. If the jump location in physical space is parameter dependent or random, standard approximation techniques like reduced basis methods, PODs, polynomial chaos, etc. are known to yield poor convergence rates. In order to improve these rates, we propose a new approximation scheme. As reduced basis methods, it relies on snapshots for the reconstruction of parameter dependent functions so that it is efficiently applicable in a PDE context. However, we allow a transformation of the physical coordinates before the use of a snapshot in the reconstruction, which allows to realign the moving discontinuities and yields high convergence rates. The transforms are automatically computed by minimizing a training error. In order to show feasibility of this approach it is tested by 1d and 2d numerical experiments.