DiffEqOperators.jl
SciMLTutorials.jl
| DiffEqOperators.jl | SciMLTutorials.jl | |
|---|---|---|
| 3 | 1 | |
| 281 | 724 | |
| - | 0.6% | |
| 4.6 | 1.5 | |
| over 1 year ago | 9 months ago | |
| Julia | CSS | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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DiffEqOperators.jl
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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.
SciMLTutorials.jl
What are some alternatives?
ReservoirComputing.jl - Reservoir computing utilities for scientific machine learning (SciML)
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.
Gridap.jl - Grid-based approximation of partial differential equations in Julia
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
FourierFlows.jl - Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains
18337 - 18.337 - Parallel Computing and Scientific Machine Learning
BoundaryValueDiffEq.jl - Boundary value problem (BVP) solvers for scientific machine learning (SciML)
OrdinaryDiffEq.jl - High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
MethodOfLines.jl - Automatic Finite Difference PDE solving with Julia SciML
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
ApproxFun.jl - Julia package for function approximation
DiffEqSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]