SciML/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
This tool helps scientists and engineers solve complex partial differential equations (PDEs) that describe physical phenomena, even when traditional methods struggle. You input your differential equations and boundary conditions, and it outputs a highly accurate numerical solution, often faster and with greater flexibility than conventional techniques. It's designed for researchers, modelers, and simulation specialists who need to understand and predict behavior in systems governed by differential equations, without needing deep expertise in advanced numerical solvers.
1,175 stars. Actively maintained with 37 commits in the last 30 days.
Use this if you need to accurately solve complex (partial) differential equations or stochastic equations, especially when dealing with high-dimensional problems or scenarios where traditional numerical methods are too slow or impractical.
Not ideal if you prefer simple, well-established numerical methods for basic ODEs/PDEs or if you are not comfortable with machine learning concepts for scientific computing.
Stars
1,175
Forks
235
Language
Julia
License
—
Category
Last pushed
Feb 25, 2026
Commits (30d)
37
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/SciML/NeuralPDE.jl"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related frameworks
NVIDIA/physicsnemo
Open-source deep-learning framework for building, training, and fine-tuning deep learning models...
NVIDIA/physicsnemo-sym
Framework providing pythonic APIs, algorithms and utilities to be used with PhysicsNeMo core to...
idrl-lab/idrlnet
IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural...
mathLab/PINA
Physics-Informed Neural networks for Advanced modeling
jdtoscano94/NABLA-SciML
Physics Informed Machine Learning Tutorials (Pytorch and Jax)