Qingyu Qu's repositories
OrdinaryDiffEq.jl
High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
BoundaryValueDiffEq.jl
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
DiffEqDocs.jl
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
ErikQQY.github.io
My own blog
FractionalDiffEq.jl
Solve Fractional Differential Equations using high performance numerical methods
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
SciMLBase.jl
The Base interface of the SciML ecosystem
SciMLBenchmarks.jl
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
StochasticDiffEq.jl
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Surrogates.jl
Surrogate modeling and optimization for scientific machine learning (SciML)
Catalyst.jl
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
DiffEqDevTools.jl
Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
DiffEqFlux.jl
Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
DiffEqProblemLibrary.jl
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
FractionalSystems.jl
Fractional order systems toolbox in Julia.
LinearSolve.jl
LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
MATLABDiffEq.jl
Common interface bindings for the MATLAB ODE solvers via MATLAB.jl for the SciML Scientific Machine Learning ecosystem
MatrixEquations.jl
Solution of Lyapunov, Sylvester and Riccati matrix equations using Julia
NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
picx-images-hosting
PicX images hosting repository
pyswarms
A research toolkit for particle swarm optimization in Python
RecursiveArrayTools.jl
Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
SciMLOperators.jl
SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia
SIAMFANLEquations.jl
This is a Julia package of nonlinear solvers. These codes are used in my book, Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia.
SimpleDiffEq.jl
Simple differential equation solvers in native Julia for scientific machine learning (SciML)
SimpleNonlinearSolve.jl
Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
toqito
|toqito> (Theory of Quantum Information Toolkit) in Python :snake: