scope VS NeuralPDE.jl

Compare scope vs NeuralPDE.jl and see what are their differences.

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scope NeuralPDE.jl
4 10
5,811 891
0.1% 2.9%
0.0 9.7
9 months ago 6 days ago
Go Julia
Apache License 2.0 GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

scope

Posts with mentions or reviews of scope. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-12.

NeuralPDE.jl

Posts with mentions or reviews of NeuralPDE.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-26.
  • from Wolfram Mathematica to Julia
    2 projects | /r/Julia | 26 May 2022
    PDE solving libraries are MethodOfLines.jl and NeuralPDE.jl. NeuralPDE is very general but not very fast (it's a limitation of the method, PINNs are just slow). MethodOfLines is still somewhat under development but generates quite fast code.
  • IA et Calcul scientifique dans Kubernetes avec le langage Julia, K8sClusterManagers.jl
    11 projects | dev.to | 12 Mar 2022
    GitHub - SciML/NeuralPDE.jl: Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
    11 projects | dev.to | 12 Mar 2022
    NeuralPDE.jl: Scientific Machine Learning for Partial Differential Equations
  • [Research] Input Arbitrary PDE -> Output Approximate Solution
    4 projects | /r/MachineLearning | 10 Jul 2021
    PDEs are difficult because you don't have a simple numerical definition over all PDEs because they can be defined by arbitrarily many functions. u' = Laplace u + f? Define f. u' = g(u) * Laplace u + f? Define f and g. Etc. To cover the space of PDEs you have to go symbolic at some point, and make the discretization methods dependent on the symbolic form. This is precisely what the ModelingToolkit.jl ecosystem is doing. One instantiation of a discretizer on this symbolic form is NeuralPDE.jl which takes a symbolic PDESystem and generates an OptimizationProblem for a neural network which represents the solution via a Physics-Informed Neural Network (PINN).
  • [D] Has anyone worked with Physics Informed Neural Networks (PINNs)?
    3 projects | /r/MachineLearning | 21 May 2021
    NeuralPDE.jl fully automates the approach (and extensions of it, which are required to make it solve practical problems) from symbolic descriptions of PDEs, so that might be a good starting point to both learn the practical applications and get something running in a few minutes. As part of MIT 18.337 Parallel Computing and Scientific Machine Learning I gave an early lecture on physics-informed neural networks (with a two part video) describing the approach, how it works and what its challenges are. You might find those resources enlightening.
  • Doing Symbolic Math with SymPy
    8 projects | news.ycombinator.com | 8 Jan 2021
    What is great about ModelingToolkit.jl is how its used in practical ways for other packages. E.g. NeuralPDE.jl.

    Compared to SymPy, I feel that it is less of a "how do I integrate this function" package and more about "how can I build this DSL" framework.

    https://github.com/SciML/NeuralPDE.jl

What are some alternatives?

When comparing scope and NeuralPDE.jl you can also consider the following projects:

deepxde - A library for scientific machine learning and physics-informed learning

SymPy - A computer algebra system written in pure Python

ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

ReservoirComputing.jl - Reservoir computing utilities for scientific machine learning (SciML)

lynis - Lynis - Security auditing tool for Linux, macOS, and UNIX-based systems. Assists with compliance testing (HIPAA/ISO27001/PCI DSS) and system hardening. Agentless, and installation optional.

AMDGPU.jl - AMD GPU (ROCm) programming in Julia

18337 - 18.337 - Parallel Computing and Scientific Machine Learning

Gridap.jl - Grid-based approximation of partial differential equations in Julia

falco - Cloud Native Runtime Security

Pyston - A faster and highly-compatible implementation of the Python programming language.

auto-07p - AUTO is a publicly available software for continuation and bifurcation problems in ordinary differential equations originally written in 1980 and widely used in the dynamical systems community.

branca - :key: Secure alternative to JWT. Authenticated Encrypted API Tokens for Go.