StochasticDiffEq.jl
DiffEqOperators.jl
| StochasticDiffEq.jl | DiffEqOperators.jl | |
|---|---|---|
| 1 | 3 | |
| 235 | 281 | |
| 0.9% | - | |
| 7.8 | 4.6 | |
| 11 days ago | 11 months ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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StochasticDiffEq.jl
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Writing unit tests in scientific computing
For stochastic processes you have to work a little bit more. However maybe the StochasticDiffEq.jl package can give some guiding there https://github.com/SciML/StochasticDiffEq.jl/tree/master/test
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.
What are some alternatives?
SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Gridap.jl - Grid-based approximation of partial differential equations in Julia
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
BoundaryValueDiffEq.jl - Boundary value problem (BVP) solvers for scientific machine learning (SciML)
SciMLSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
ApproxFun.jl - Julia package for function approximation
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)
FourierFlows.jl - Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains
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]
julia - The Julia Programming Language
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.
oxide-enzyme - Enzyme integration into Rust. Experimental, do not use.