Lists (1)
Sort Name ascending (A-Z)
Stars
- All languages
- Assembly
- Batchfile
- C
- C#
- C++
- CMake
- CSS
- Csound Document
- Cuda
- Cython
- Dockerfile
- Emacs Lisp
- Fortran
- Go
- Groovy
- HTML
- Haskell
- Java
- JavaScript
- Julia
- Jupyter Notebook
- Lua
- MATLAB
- Markdown
- Max
- OCaml
- Objective-C
- PHP
- PostScript
- PureBasic
- Python
- R
- RMarkdown
- Roff
- Rust
- SCSS
- Shell
- Singularity
- Stan
- Standard ML
- Swift
- Tcl
- TeX
- TypeScript
- Typst
- VBScript
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…
Interactive data visualizations and plotting in Julia
Powerful convenience for Julia visualizations and data analysis
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 a…
Automatically update function definitions in a running Julia session
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
Book on Julia for Data Science
An algebraic spin on grammar-of-graphics data visualization in Julia. Powered by the Makie.jl plotting ecosystem.
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
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…
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Reservoir computing utilities for scientific machine learning (SciML)
Bayesian Statistics using Julia and Turing
Contributor's Guide on Collaborative Practices for Community Packages
Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)
A course on composable system modeling, differential-algebraic equations, acausal modeling, compilers for simulation, and building digital twins of real-world devices
Makie topo plot recipes, for neuro-science, geo plots and anyone needing surface plots from unstructured data