DifferentialEquations.jl
kotlin
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DifferentialEquations.jl | kotlin | |
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6 | 208 | |
2,754 | 47,471 | |
1.5% | 0.9% | |
7.3 | 10.0 | |
18 days ago | 3 days ago | |
Julia | Kotlin | |
GNU General Public License v3.0 or later | - |
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DifferentialEquations.jl
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Startups are building with the Julia Programming Language
This lists some of its unique abilities:
https://docs.sciml.ai/DiffEqDocs/stable/
The routines are sufficiently generic, with regard to Julia’s type system, to allow the solvers to automatically compose with other packages and to seamlessly use types other than Numbers. For example, instead of handling just functions Number→Number, you can define your ODE in terms of quantities with physical dimensions, uncertainties, quaternions, etc., and it will just work (for example, propagating uncertainties correctly to the solution¹). Recent developments involve research into the automated selection of solution routines based on the properties of the ODE, something that seems really next-level to me.
[1] https://lwn.net/Articles/834571/
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From Common Lisp to Julia
https://github.com/SciML/DifferentialEquations.jl/issues/786. As you could see from the tweet, it's now at 0.1 seconds. That has been within one year.
Also, if you take a look at a tutorial, say the tutorial video from 2018,
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When is julia getting proper precompilation?
It's not faith, and it's not all from Julia itself. https://github.com/SciML/DifferentialEquations.jl/issues/785 should reduce compile times of what OP mentioned for example.
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Julia 1.7 has been released
Let's even put raw numbers to it. DifferentialEquations.jl usage has seen compile times drop from 22 seconds to 3 seconds over the last few months.
https://github.com/SciML/DifferentialEquations.jl/issues/786
- Suggest me a Good library for scientific computing in Julia with good support for multi-core CPUs and GPUs.
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DifferentialEquations compilation issue in Julia 1.6
https://github.com/SciML/DifferentialEquations.jl/issues/737 double posted, with the answer here. Please don't do that.
kotlin
- Kotlin 2.0 RC1
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Implementing an Auto-logout Feature for Android in Kotlin
A basic understanding of Kotlin and programming in general (OOP).
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Kotlin and Azure Functions - Automating the deployment
Being somewhat allergic to coding in Java (this is a personal thing, if you like Java then good for you) I decided to try out writing the code using Kotlin from JetBrains instead. I'm already using IntelliJ as I work with Apache Spark using Scala, so the tooling was already there and ready to go for this.
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Top Paying Programming Technologies 2024
25. Kotlin - $78,207
- Fuckjava.com Redirects to Kotlinlang.org
- Kotlin 2.0.0 Beta 2
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Tests Everywhere - Kotlin
Kotlin testing with Kotest and MockK
- Kotlin 2.0.0 Beta1 is out
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🎉 Kotlin Multiplatform is now STABLE!
Congrats to our friends at Kotlin. 🚀 After years of growth and development, KMP reaches a pivotal milestone with 1.9.20. We’ve been on team Kotlin Multiplatform since day one, and the best is yet to come! Learn more 👉 https://touchlab.co/kotlin-multiplatform-is-stable
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Regarding Lenses, Prisms and Optics
Another option could be to check out Kotlin. It's a JVM language that while still object-oriented has may functional syntax features.
What are some alternatives?
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
solidity - Solidity, the Smart Contract Programming Language
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
scala - Scala 2 compiler and standard library. Bugs at https://github.com/scala/bug; Scala 3 at https://github.com/scala/scala3
Gridap.jl - Grid-based approximation of partial differential equations in Julia
Flask - The Python micro framework for building web applications.
ApproxFun.jl - Julia package for function approximation
puppeteer - Node.js API for Chrome
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
kotlinx.coroutines - Library support for Kotlin coroutines
FFTW.jl - Julia bindings to the FFTW library for fast Fourier transforms
Express - Fast, unopinionated, minimalist web framework for node.