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DiffEqOperators.jl Alternatives
Similar projects and alternatives to DiffEqOperators.jl
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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
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SciMLBenchmarks.jl
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
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FourierFlows.jl
Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains
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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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SciMLTutorials.jl
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better DiffEqOperators.jl alternative or higher similarity.
DiffEqOperators.jl discussion
DiffEqOperators.jl reviews and mentions
Posts with mentions or reviews of DiffEqOperators.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-11-30.
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Julia 1.7 has been released
>I hope those benchmarks are coming in hot
M1 is extremely good for PDEs because of its large cache lines.
https://github.com/SciML/DiffEqOperators.jl/issues/407#issue...
The JuliaSIMD tools which are internally used for BLAS instead of OpenBLAS and MKL (because they tend to outperform standard BLAS's for the operations we use https://github.com/YingboMa/RecursiveFactorization.jl/pull/2...) also generate good code for M1, so that was giving us some powerful use cases right off the bat even before the heroics allowed C/Fortran compilers to fully work on M1.
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Why are NonlinearSolve.jl and DiffEqOperators.jl incompatible with the latest versions of ModelingToolkit and Symbolics!!!? Symbolics and ModelingToolkit are heavily downgraded when those packages are added.
(b) DiffEqOperators.jl is being worked on https://github.com/SciML/DiffEqOperators.jl/pull/467 .
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What's Bad about Julia?
I like that they are colored now, but really what needs to be added is type parameter collapasing. In most cases, you want to see `::Dual{...}`, i.e. "it's a dual number", not `::Dual{typeof(ODESolution{sfjeoisjfsfsjslikj},sfsef,sefs}` (these can literally get to 3000 characters long). As an example of this, see the stacktraces in something like https://github.com/SciML/DiffEqOperators.jl/issues/419 . The thing is that it gives back more type information than the strictest dispatch: no function is dispatching off of that first 3000 character type parameter, so you know that printing that chunk of information is actually not informative to any method decisions. Automated type abbreviations could take that heuristic and chop out a lot of the cruft.
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Stats
Basic DiffEqOperators.jl repo stats
3
281
4.6
over 1 year ago
SciML/DiffEqOperators.jl is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of DiffEqOperators.jl is Julia.
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