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On computational inversion with metrics from optimal transport

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RNTW04 - Synergistic workshop on Rich and Nonlinear tomography aimed at drawing together all strands of both methods and applications with new insights

Metrics originated from optimal transport theory have been used recently in solving computational inverse problems related to PDEs. Some obvious advantages (such as its stability against high-frequency noise) and disadvantages (such as the loss of resolution when iterations are prematurely stopped) over the classical L2-based least-squares method have been reported, especially in applications in full waveform inversion. I will discuss some recent understanding of some aspects of computational inversion with optimal transport, especially on its advantage on the optimization landscape.

This talk is part of the Isaac Newton Institute Seminar Series series.

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