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Catalyst.jl
Chemical reaction network and systems biology interface
...Generated models can be used with solvers throughout the broader SciML ecosystem, including higher-level SciML packages (e.g. for sensitivity analysis, parameter estimation, machine learning applications, etc). -
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
...Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger. -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function spaces. ... -
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TensorBoardLogger.jl
Easy peasy logging to TensorBoard with Julia
TensorBoardLogger.jl is a native library for logging arbitrary data to Tensorboard, extending Julia's standard Logging framework. It can also be used to deserialize TensoBoard's .proto files. The fundamental type defined in this package is a TBLogger, which behaves like other standard loggers in Julia such as ConsoleLogger or TextLogger. You can create one by passing it the path to the folder where you want to store the data. You can also pass an optional second argument to specify the behaviour in case there already exists a document at the given path. -
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DynamicQuantities.jl
Lightweight + fast physical quantities in Julia
DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods. -
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Zygote
21st century AD
Zygote provides source-to-source automatic differentiation (AD) in Julia, and is the next-gen AD system for the Flux differentiable programming framework. For more details and benchmarks of Zygote's technique, see our paper. You may want to check out Flux for more interesting examples of Zygote usage; the documentation here focuses on internals and advanced AD usage. -
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Coverage.jl
Take Julia code coverage and memory allocation results, do useful thin
Julia can track how many times, if any, each line of your code is run. This is useful for measuring how much of your code base your tests actually test, and can reveal the parts of your code that are not tested and might be hiding a bug. You can use Coverage.jl to summarize the results of this tracking or to send them to a service like Coveralls.io or Codecov.io. -
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Manifolds.jl
Manifolds.jl provides a library of manifolds
...The implemented manifolds are accompanied by their mathematical formulae. The manifolds are implemented using the interface for manifolds given in ManifoldsBase.jl. You can use that interface to implement your own software on manifolds, such that all manifolds based on that interface can be used within your code. -
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StructuralEquationModels.jl
A fast and flexible Structural Equation Modelling Framework
This is a package for Structural Equation Modeling in development. It is written for extensibility, that is, you can easily define your own objective functions and other parts of the model. At the same time, it is (very) fast. We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof. As a user, you can easily define custom loss functions. For those, you can decide to provide analytical gradients or use finite difference approximation / automatic differentiation. ... -
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Oscar.jl
A comprehensive open source computer algebra system for computations
Welcome to the OSCAR project, a visionary new computer algebra system that combines the capabilities of four cornerstone systems: GAP, Polymake, Antic and Singular. OSCAR requires Julia 1.6 or newer. In principle it can be installed and used like any other Julia package; doing so will take a couple of minutes. A comprehensive open source computer algebra system for computations in algebra, geometry, and number theory. -
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Pluto.jl
Simple reactive notebooks for Julia plutojl.org
We are on a mission to make scientific computing more accessible and fun. Writing a notebook is not just about writing the final document, Pluto empowers the experiments and discoveries that are essential to getting there. -
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
...Libraries from Python, R, C/Fortran, C++, and Java can also be used. -
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Query.jl
Query almost anything in julia
Query is a package for querying julia data sources. It can filter, project, join and group data from any iterable data source, including all the sources supported in IterableTables.jl. One can for example query any of the following data sources: any array, DataFrames, DataStreams (including CSV, Feather, SQLite, ODBC), DataTables, IndexedTables, TimeSeries, Temporal, TypedTables and DifferentialEquations (any DESolution). -
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Gen.jl
A general-purpose probabilistic programming system
...Gen features an easy-to-use modeling language for writing down generative models, inference models, variational families, and proposal distributions using ordinary code. But it also lets users migrate parts of their model or inference algorithm to specialized modeling languages for which it can generate especially fast code. Users can also hand-code parts of their models that demand better performance. Neural network inference is fast, but can be inaccurate on out-of-distribution data, and requires expensive training. -
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SymbolicUtils.jl
Symbolic expressions, rewriting and simplification
...If you want to build a crazy CAS for your weird Octonian algebras, you've come to the right place. Symbols in SymbolicUtils carry type information. Operations on them propagate this information. A rule-based rewriting language can be used to find subexpressions that satisfy arbitrary conditions and apply arbitrary transformations on the matches. The library also contains a set of useful simplification rules for expressions of numeric symbols and numbers. These can be remixed and extended for special purposes. -
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PackageCompiler
Compile your Julia Package
Julia is, in general, a "just-barely-ahead-of-time" compiled language. When you call a function for the first time, Julia compiles it for precisely the types of arguments given. This can take some time. All subsequent calls within that same session use this fast compiled function, but if you restart Julia you lose all the compiled work. PackageCompiler allows you to do this work upfront — further ahead of time — and store the results for a lower latency startup. You can save loaded packages and compiled functions into a file (called a sysimage) that you pass to Julia upon startup. ... -
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Books.jl
Create books with Julia
In a nutshell, this package is meant to generate books (or reports or dashboards) with embedded Julia output. Via Pandoc, the package can live serve a website and build various outputs including a website and PDF. For many standard output types, such as DataFrames and plots, the package can run your code and will automatically handle proper embedding in the output documents, and also try to guess suitable captions and labels. Also, it is possible to work via the live server, which shows changes within seconds. -
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Pkg Julia
Package manager for the Julia programming language
...In Pkg, since each project maintains its own independent set of package versions, you’ll never have this problem again. Moreover, if you check out a project on a new system, you can simply materialize the environment described by its manifest file and immediately be up and running. -
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LinearOperators.jl
Linear Operators for Julia
Operators behave like matrices (with some exceptions - see below) but are defined by their effect when applied to a vector. They can be transposed, conjugated, or combined with other operators cheaply. The costly operation is deferred until multiplied with a vector. -
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Winston.jl
2D plotting for Julia
2D plotting for Julia. Winston offers an easy-to-use plot command to create figures without any fuss. After Winston is loaded by typing using Winston, the most basic plot can be created. -
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Calculus.jl
Calculus functions in Julia
The Calculus package provides tools for working with the basic calculus operations of differentiation and integration. You can use the Calculus package to produce approximate derivatives by several forms of finite differencing or to produce exact derivatives using symbolic differentiation. You can also compute definite integrals by different numerical methods. -
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Dash Bootstrap Components
Bootstrap components for Plotly Dash
dash-bootstrap-components is a library of Bootstrap components for use with Plotly Dash, that makes it easier to build consistently styled Dash apps with complex, responsive layouts. -
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LLVM.jl
Julia wrapper for the LLVM C API
A Julia wrapper for the LLVM C API. The LLVM.jl package is a Julia wrapper for the LLVM C API, and can be used to work with the LLVM compiler framework from Julia. You can use the package to work with LLVM code generated by Julia, to interoperate with the Julia compiler, or to create your own compiler. It is heavily used by the different GPU compilers for the Julia programming language. -
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Integrals.jl
A common interface for quadrature and numerical integration
Integrals.jl is an instantiation of the SciML common IntegralProblem interface for the common numerical integration packages of Julia, including both those based upon quadrature as well as Monte-Carlo approaches. By using Integrals.jl, you get a single predictable interface where many of the arguments are standardized throughout the various integrator libraries. This can be useful for benchmarking or for library implementations since libraries that internally use a quadrature can easily accept an integration method as an argument. -
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HCubature.jl
Pure-Julia multidimensional h-adaptive integration
The HCubature module is a pure-Julia implementation of multidimensional "h-adaptive" integration. then hcubature(f, a, b) computes the integral, adaptively subdividing the integration volume into smaller and smaller pieces until convergence is achieved to the desired tolerance (specified by optional rtol and atol keyword arguments. Because hcubature is written purely in Julia, the integrand f(x) can return any vector-like object (technically, any type supporting +, -, * real, and norm: a Banach space). You can integrate real, complex, and matrix-valued integrands, for example. Note that HCubature assumes that your function f(x) can be computed at arbitrary points in the integration domain. (This is the ideal way to do numerical integration.) ...
