68 research outputs found
Probabilistic ODE Solvers with Runge-Kutta Means
Runge-Kutta methods are the classic family of solvers for ordinary
differential equations (ODEs), and the basis for the state of the art. Like
most numerical methods, they return point estimates. We construct a family of
probabilistic numerical methods that instead return a Gauss-Markov process
defining a probability distribution over the ODE solution. In contrast to prior
work, we construct this family such that posterior means match the outputs of
the Runge-Kutta family exactly, thus inheriting their proven good properties.
Remaining degrees of freedom not identified by the match to Runge-Kutta are
chosen such that the posterior probability measure fits the observed structure
of the ODE. Our results shed light on the structure of Runge-Kutta solvers from
a new direction, provide a richer, probabilistic output, have low computational
cost, and raise new research questions.Comment: 18 pages (9 page conference paper, plus supplements); appears in
Advances in Neural Information Processing Systems (NIPS), 201
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