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Black Box Probabilistic Numerics
Onur Teymur · Christopher Foley · Philip Breen · Toni Karvonen · Chris Oates

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @

Probabilistic numerics casts numerical tasks, such the numerical solution of differential equations, as inference problems to be solved. One approach is to model the unknown quantity of interest as a random variable, and to constrain this variable using data generated during the course of a traditional numerical method. However, data may be nonlinearly related to the quantity of interest, rendering the proper conditioning of random variables difficult and limiting the range of numerical tasks that can be addressed. Instead, this paper proposes to construct probabilistic numerical methods based only on the final output from a traditional method. A convergent sequence of approximations to the quantity of interest constitute a dataset, from which the limiting quantity of interest can be extrapolated, in a probabilistic analogue of Richardson’s deferred approach to the limit. This black box approach (1) massively expands the range of tasks to which probabilistic numerics can be applied, (2) inherits the features and performance of state-of-the-art numerical methods, and (3) enables provably higher orders of convergence to be achieved. Applications are presented for nonlinear ordinary and partial differential equations, as well as for eigenvalue problems—a setting for which no probabilistic numerical methods have yet been developed.

Author Information

Onur Teymur (University of Kent)
Christopher Foley (Optima Partners)
Philip Breen (Roar AI)
Toni Karvonen (University of Helsinki)
Chris Oates (Newcastle University)

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