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

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

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