HighDimPDE.jl is a Julia package to solve Highly Dimensional non-linear, non-local 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 $u \colon [0,T] \times \Omega \to \R$, $\Omega \subseteq \R^d$ is subject to initial and boundary conditions, and where $d$ is large.

HighDimPDE.jl implements solver algorithms that break down the curse of dimensionality, including

To make the most out of HighDimPDE.jl, we advise to first have a look at the

as all solver algorithms heavily rely on it.

Algorithm overview

Time discretization free
Single point $x \in \R^d$ approximation
$d$-dimensional cube $[a,b]^d$ approximation
Gradient non-linearities✔️

✔️ : will be supported in the future