Krotov.jl
Gridap.jl
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Krotov.jl | Gridap.jl | |
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1 | 2 | |
7 | 629 | |
- | 1.4% | |
6.1 | 9.2 | |
24 days ago | 7 days ago | |
Julia | Julia | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Krotov.jl
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CamelCase vs. Underscores: Revisited
I would totally want that! Specifically, `a+b = a + b` where `a + b` is an expensive operation. Julia allows stuff like that to some extent, e.g. https://github.com/JuliaQuantumControl/Krotov.jl/blob/3132f0..., but unfortunately not with `+`
Gridap.jl
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Best free/open source CAS ?
Another I've been working on learning is Julia, which aims to use a syntax very similar to how you'd write it mathematically, and I like being able to include units in calculations using the unitful.jl package, and there are FEM packages available like Gridap.
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[Research] Input Arbitrary PDE -> Output Approximate Solution
PINN methods are absurdly slow (DeepXDE is about 10,000x slower than an ODE solver for example, while using implicit parallelism vs serial ODE solver) but they are flexible. So ModelingToolkit.jl has alternative options, like DiffEqOperators.jl takes the same specification and generates ODESystem and NonlinearSystem problems via finite difference discretizations (known as "method of lines"). There's a (pseudo-)spectral part of the interface coming relatively soon as well, with GridAP.jl integration for FEM coming soon. So this is made to be a universal arbitrary PDE -> approximate solution interface which is generic to the method and solving process.
What are some alternatives?
dolfinx - Next generation FEniCS problem solving environment
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
ApproxFun.jl - Julia package for function approximation
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
FourierFlows.jl - Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
julia - The Julia Programming Language
QuantumOptics.jl - Library for the numerical simulation of closed as well as open quantum systems.
Pulses.jl - A tiny quantum optimal control library.
NumericalAlgorithms.jl - [DEPRECATED] Statistics & Numerical algorithms implemented in Julia.
slint - Slint is a declarative GUI toolkit to build native user interfaces for Rust, C++, or JavaScript apps.