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Extracting spatiotemporal patterns from data with dynamics-adapted kernels

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If you have a question about this talk, please contact Mustapha Amrani.

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Kernel methods provide an attractive way of extracting features from data by biasing the geometry of the data in a controlled manner. In this talk, we discuss a family of kernels for dynamical systems featuring an explicit dependence on the dynamical vector field operating in the phase-space manifold, estimated empirically through finite differences of time-ordered data samples. In a suitable asymptotic limit, the associated diffusion operator generates diffusions along the integral curves of the dynamical vector field. We present applications to toy dynamical systems and data generated by comprehensive climate models.

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

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