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Workshop: Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems

Gaussian processes at the Helm(holtz): A better way to model ocean currents

Renato Berlinghieri · Tamara Broderick · Ryan Giordano · Tamay Ozgokmen · Kaushik Srinivasan · Brian Trippe · Junfei Xia


Understanding the behavior of ocean currents has important practical applications. Since we expect current dynamics to be smooth but highly non-linear, Gaussian processes (GPs) offer an attractive model. In particular, one existing approach is to consider the velocities of the buoys as sparse observations of a vector field in two spatial dimensions and one time dimension. But we show that applying a GP, e.g. with a standard square exponential kernel, directly to this data fails to capture real-life current structure, such as continuity of currents and the shape of vortices. By contrast, these physical properties are captured by divergence and curl-free components of a vector field obtained through a Helmholtz decomposition. So we propose instead to model these components with a GP directly. We show that, because this decomposition relates to the original vector field just via mixed partial derivatives, we can still perform inference given the original data with only a small constant multiple of additional computational expense. We illustrate our method on real oceans data.

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