Home

Awesome

Join the chat at https://julialang.zulipchat.com #sciml-bridged Global Docs

codecov Build Status

ColPrac: Contributor's Guide on Collaborative Practices for Community Packages SciML Code Style

HighDimPDE.jl

HighDimPDE.jl is a Julia package to solve Highly Dimensional non-local, non-linear PDEs of the form

$$ \begin{aligned} (\partial_t u)(t,x) &= \int_{\Omega} f\big(t,x,{\bf x}, u(t,x),u(t,{\bf x}), ( \nabla_x u )(t,x ),( \nabla_x u )(t,{\bf x} ) \big) d{\bf x} \ & \quad + \big\langle \mu(t,x), ( \nabla_x u )( t,x ) \big\rangle + \tfrac{1}{2} \text{Trace} \big(\sigma(t,x) [ \sigma(t,x) ]^* ( \text{Hess}_x u)(t, x ) \big). \end{aligned} $$

where $u \colon [0,T] \times \Omega \to \mathbb{R}, \Omega \subseteq \mathbb{R}^{d}$ is subject to initial and boundary conditions, and where $d$ is large.

Tutorials and Documentation

For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation, which contains the unreleased features.

Installation

Open Julia and type the following

using Pkg;
Pkg.add("HighDimPDE.jl")

This will download the latest version from the git repo and download all dependencies.

Getting started

See documentation and test folders.

Reference

<!-- - Becker, S., Braunwarth, R., Hutzenthaler, M., Jentzen, A., von Wurstemberger, P., Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations. [arXiv](https://arxiv.org/abs/2005.10206) (2020) - Beck, C., Becker, S., Cheridito, P., Jentzen, A., Neufeld, A., Deep splitting method for parabolic PDEs. [arXiv](https://arxiv.org/abs/1907.03452) (2019) - Han, J., Jentzen, A., E, W., Solving high-dimensional partial differential equations using deep learning. [arXiv](https://arxiv.org/abs/1707.02568) (2018) --> <!-- ## Acknowledgements `HighDimPDE.jl` is inspired from Sebastian Becker's scripts in Python, TensorFlow, and C++. Pr. Arnulf Jentzen largely contributed to the theoretical developments of the solver algorithms implemented. -->