MethodOfLines.jl VS ParallelKMeans.jl

Compare MethodOfLines.jl vs ParallelKMeans.jl and see what are their differences.

MethodOfLines.jl

Automatic Finite Difference PDE solving with Julia SciML (by SciML)
Pde finite-difference-method Sciml scientific-ml partial-differential-equations Julia high-performance-computing symbolic-computation parallel-computing
Source Code
docs.sciml.ai
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ParallelKMeans.jl

Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm (by PyDataBlog)
Clustering kmeans-clustering Julia parallel-computing mlj-unsupervised Mlj kmeans-clustering-algorithm
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MethodOfLines.jl ParallelKMeans.jl
2 1
149 50
0.7% -
9.2 3.1
5 days ago 12 months ago
Julia Julia
MIT License MIT License
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MethodOfLines.jl

Posts with mentions or reviews of MethodOfLines.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.
  • Please help me make a case to implement Julia in enterprise
    1 project | /r/Julia | 7 Nov 2022
    You might be interested in MethodOfLines.jl, a symbolic automatic partial differential equation discretizer based on the ModelingToolkit and DiffEq stack.
  • 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.

ParallelKMeans.jl

Posts with mentions or reviews of ParallelKMeans.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-05.
  • Is Julia suitable for computational physics?
    4 projects | /r/Julia | 5 Jan 2021
    Once upon a time we implemented kmeans algorithm in our Julia and it outperform c implementations by a large amount. The reason for that is not that Julia is faster (which is not), but mainly because we were able to better utilize resources that we have. One can rewrite our Julia solution in c and get better timings, but I guess this solution is not as obvious from the c perspective. Package in question is https://github.com/PyDataBlog/ParallelKMeans.jl

What are some alternatives?

When comparing MethodOfLines.jl and ParallelKMeans.jl you can also consider the following projects:

DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)

MLJ.jl - A Julia machine learning framework

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

BlockBandedMatrices.jl - A Julia package for representing block-banded matrices and banded-block-banded matrices

JFVM.jl - A simple finite volume tool for Julia

ScientificTypes.jl - An API for dispatching on the "scientific" type of data instead of the machine type

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

ClimateMachine.jl - Climate Machine: an Earth System Model that automatically learns from data

NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

MethodOfLines.jl vs DiffEqOperators.jl ParallelKMeans.jl vs MLJ.jl MethodOfLines.jl vs SciMLBenchmarks.jl ParallelKMeans.jl vs BlockBandedMatrices.jl MethodOfLines.jl vs JFVM.jl ParallelKMeans.jl vs ScientificTypes.jl MethodOfLines.jl vs ModelingToolkit.jl ParallelKMeans.jl vs ClimateMachine.jl MethodOfLines.jl vs NeuralPDE.jl