SciML/HighDimPDE.jl
A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality
This tool helps researchers and quantitative analysts solve complex partial differential equations (PDEs) that have many variables. It takes the mathematical definition of a high-dimensional PDE and provides a numerical solution for it. This is particularly useful for scientists, engineers, and financial modelers working with systems where many factors influence the outcome, overcoming traditional computational limits.
Use this if you need to find numerical solutions for highly-dimensional, non-local, or non-linear partial differential equations that are otherwise too complex to solve efficiently.
Not ideal if you are working with low-dimensional PDEs or require analytical solutions rather than numerical approximations.
Stars
84
Forks
10
Language
Julia
License
—
Category
Last pushed
Feb 20, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/SciML/HighDimPDE.jl"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lululxvi/deepxde
A library for scientific machine learning and physics-informed learning
pnnl/neuromancer
Pytorch-based framework for solving parametric constrained optimization problems,...
wilsonrljr/sysidentpy
A Python Package For System Identification Using NARMAX Models
dynamicslab/pysindy
A package for the sparse identification of nonlinear dynamical systems from data
google-research/torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.