Top 15 neural-ode Open-Source Projects

• SciMLBook

  4 1,796 4.9 HTML

  Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

  

• torchdyn

  1 1,283 4.8 Jupyter Notebook

  A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

  

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• DiffEqFlux.jl

  2 839 8.9 Julia

  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

  

• CfC

  7 796 10.0 Python

  Closed-form Continuous-time Neural Networks

Project mention: LNNs - Liquid Neural Networks: Seeking general advice, papers, implementations | /r/datascience | 2023-07-10

And here's the repo: https://github.com/raminmh/CfC

• SciMLTutorials.jl

  1 709 1.5 CSS

  Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

  

• SciMLSensitivity.jl

  2 316 9.5 Julia

  A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

  

• 18S096SciML

  1 303 2.7 HTML

  18.S096 - Applications of Scientific Machine Learning

  

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

• DiffEqBase.jl

  1 298 9.3 Julia

  The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

  

• SciMLBenchmarks.jl

  10 292 9.7 MATLAB

  Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

  

• ComponentArrays.jl

  1 277 7.0 Julia

  Arrays with arbitrarily nested named components.

• DiffEqGPU.jl

  2 267 8.1 Julia

  GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

  

Project mention: 2023 was the year that GPUs stood still | news.ycombinator.com | 2023-12-29

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.

• torchode

  1 192 5.4 Python

  A parallel ODE solver for PyTorch

• finite-element-networks

  1 60 1.8 Jupyter Notebook

  Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022

• BoundaryValueDiffEq.jl

  1 40 9.3 Julia

  Boundary value problem (BVP) solvers for scientific machine learning (SciML)

  

• LT-OCF

  1 20 10.0 Python

  LT-OCF: Learnable-Time ODE-based Collaborative Filtering, CIKM'21

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

neural-ode related posts

• Machine learning and black box numerical solver[D]

  2 projects | /r/MachineLearning | 26 Jan 2023

• Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation - Chris Rackauckas (ASA Statistical Computing & Graphics Sections)

  1 project | /r/Julia | 7 Aug 2022

• Why Fortran is easy to learn

  19 projects | news.ycombinator.com | 7 Jan 2022

• Composability in Julia: Implementing Deep Equilibrium Models via Neural Odes

  2 projects | news.ycombinator.com | 21 Oct 2021

• [R] New directions in Neural Differential Equations

  1 project | /r/MachineLearning | 19 May 2021

• JuliaSim - Simulating Reality (new product by Julia Computing)

  1 project | /r/Julia | 7 Apr 2021

• Old programming language is suddenly getting more popular again

  3 projects | news.ycombinator.com | 6 Apr 2021
• A note from our sponsor - SaaSHub
  www.saashub.com | 14 May 2024

  SaaSHub helps you find the best software and product alternatives Learn more →

Index

Sponsored

SaaSHub - Software Alternatives and Reviews

SaaSHub helps you find the best software and product alternatives

www.saashub.com