MethodOfLines.jl
JFVM.jl
| MethodOfLines.jl | JFVM.jl | |
|---|---|---|
| 2 | 1 | |
| 149 | 42 | |
| 0.7% | - | |
| 9.2 | 0.0 | |
| 4 days ago | about 1 year ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
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MethodOfLines.jl
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Please help me make a case to implement Julia in enterprise
You might be interested in MethodOfLines.jl, a symbolic automatic partial differential equation discretizer based on the ModelingToolkit and DiffEq stack.
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from Wolfram Mathematica to Julia
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.
JFVM.jl
What are some alternatives?
ParallelKMeans.jl - Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
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.
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
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems