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Top 11 neural-differential-equation Open-Source Projects
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DifferentialEquations.jl
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
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torchdyn
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
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diffrax
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
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NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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DiffEqFlux.jl
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
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SciMLTutorials.jl
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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NeuralCDE
Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
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DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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DiffEqGPU.jl
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
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The documentation has a manifest associated with it: https://docs.sciml.ai/NeuralPDE/dev/#Reproducibility. Instantiating the manifest will give you all of the exact versions used for the documentation build (https://github.com/SciML/NeuralPDE.jl/blob/gh-pages/v5.7.0/assets/Manifest.toml). You just ]instantiate folder_of_manifest. Or you can use the Project.toml.
Indeed, and this year we created a system for compiling ODE code not just optimized CUDA kernels but also OneAPI kernels, AMD GPU kernels, and Metal. Peer reviewed version is here (https://www.sciencedirect.com/science/article/abs/pii/S00457...), open access is here (https://arxiv.org/abs/2304.06835), and the open source code is at https://github.com/SciML/DiffEqGPU.jl. The key that the paper describes is that in this case kernel generation is about 20x-100x faster than PyTorch and Jax (see the Jax compilation in multiple ways in this notebook https://colab.research.google.com/drive/1d7G-O5JX31lHbg7jTzz..., extra overhead though from calling Julia from Python but still shows a 10x).
The point really is that while deep learning libraries are amazing, at the end of the day they are DSL and really pull towards one specific way of computing and parallelization. It turns out that way of parallelizing is good for deep learning, but not for all things you may want to accelerate. Sometimes (i.e. cases that aren't dominated by large linear algebra) building problem-specific kernels is a major win, and it's over-extrapolating to see ML frameworks do well with GPUs and think that's the only thing that's required. There are many ways to parallelize a code, ML libraries hardcode a very specific way, and it's good for what they are used for but not every problem that can arise.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
neural-differential-equations related posts
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Show HN: Optimistix: Nonlinear Optimisation in Jax+Equinox
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Automatically install huge number of dependency?
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PyTorch 2.0
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[D] Adjoint Sensitivity Method vs Reverse Mode Autodiff
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Solving system of coupled differential equations using Runge-Kutta in python
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from Wolfram Mathematica to Julia
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[D] What useful personal projects have you made?
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A note from our sponsor - SaaSHub
www.saashub.com | 14 May 2024
Index
What are some of the best open-source neural-differential-equation projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | DifferentialEquations.jl | 2,769 |
| 2 | torchsde | 1,483 |
| 3 | torchdyn | 1,283 |
| 4 | diffrax | 1,249 |
| 5 | NeuralPDE.jl | 909 |
| 6 | DiffEqFlux.jl | 839 |
| 7 | SciMLTutorials.jl | 709 |
| 8 | NeuralCDE | 581 |
| 9 | DiffEqBase.jl | 298 |
| 10 | DiffEqGPU.jl | 267 |
| 11 | BoundaryValueDiffEq.jl | 40 |
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