BayesianSDEsolver

Efficient SDE samplers including Gaussian-based probabilistic solvers. Written in JAX.

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/learn @ylefay/BayesianSDEsolver

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README

Bayesian SDE solvers

Companion code in JAX to the article preprint: Modelling pathwise uncertainty of Stochastic Differential Equations samplers via Probabilistic Numerics by Yvann Le Fay, Simo Särkkä and Adrien Corenflos.

What is it?

This is a JAX implementation of 1.0 strongly convergent SDE schemes including novel Gaussian-based probabilistic SDE solvers.

Supported features

Classic SDE schemes: Euler-Maruyama, 1.5 Taylor-Itô
Exotic Gaussian filtering SDE schemes including 1.0 strongly convergent scheme based on piecewise polynomial approximations of the Brownian motion. Can be used both for pathwise and moment computations.
Euler ODE scheme.
Extended Kalman filtering, with lower square root implementation.

Usage

See the scripts and tests folders for examples of usage.

Reproducing the results of the article

Please refer to scripts/README.md for instructions on how to reproduce the results of the article.

License

This project is licensed under the MIT License.

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