Dependency Packages
-
Trixi.jl363Trixi.jl: Adaptive high-order numerical simulations of hyperbolic PDEs in Julia
-
Sundials.jl188Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner
-
LinearSolve.jl178LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
-
ReducedBasisMethods.jl1Reduced Basis Methods for Particle Systems
-
OceanBioME.jl7🌊 🦠 🌿 A fast and flexible modelling environment written in Julia for modelling the coupled interactions between ocean biogeochemistry, carbonate chemistry, and physics
-
SymbolicRegression.jl377Distributed High-Performance symbolic regression in Julia
-
ControlSystems.jl430A Control Systems Toolbox for Julia
-
ModelingToolkitStandardLibrary.jl76A standard library of components to model the world and beyond
-
SemanticModels.jl76A julia package for representing and manipulating model semantics
-
AlgebraicRelations.jl41Relational Algebra, now with more algebra!
-
Optimization.jl512Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
-
GalacticOptim.jl512Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
-
SimulationLogs.jl19Signal logging and scoping for DifferentialEquations.jl simulations.
-
Sophon.jl36Neural networks and neural operators for physics-informed machine learning
-
DataInterpolations.jl129A library of data interpolation and smoothing functions
-
Pesto.jl2Phylogenetic Estimation of Shifts in the Tempo of Origination
-
SymbolicNumericIntegration.jl93SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
-
BifurcationKit.jl240A Julia package to perform Bifurcation Analysis
-
SymbolicUtils.jl454Expression rewriting and simplification
-
Symbolics.jl1169A fast and modern CAS for a fast and modern language.
-
ClimaAtmos.jl43ClimaAtmos.jl is a library for building atmospheric circulation models that is designed from the outset to leverage data assimilation and machine learning tools. We welcome contributions!
-
ModelingToolkit.jl1212An 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
-
FundamentalsNumericalComputation.jl60Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.
-
UnitfulAstrodynamics.jl16Astrodynamics with units! Provides common astrodynamics calculations, plotting, and iterative Halo, Kepler, and Lambert solvers.
-
OrbitalTrajectories.jl74OrbitalTrajectories.jl is a modern orbital trajectory design, optimisation, and analysis library for Julia, providing methods and tools for designing spacecraft orbits and transfers via high-performance simulations of astrodynamical models.
-
GeneralAstrodynamics.jl16Astrodynamics with units! Provides common astrodynamics calculations, plotting, and iterative Halo, Kepler, and Lambert solvers.
-
DynamicalSystems.jl725Award winning software library for nonlinear dynamics and nonlinear timeseries analysis
-
HighDimPDE.jl60A Julia package that breaks down the curse of dimensionality in solving PDEs.
-
DiffEqSensitivity.jl248A 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, SDEs, DDEs, DAEs, etc.
-
SciMLSensitivity.jl248A 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, SDEs, DDEs, DAEs, etc.
-
NonlinearSolve.jl112High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
-
DataDrivenDiffEq.jl372Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
-
OrdinaryDiffEq.jl425High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
-
NeuralNetDiffEq.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
-
NeuralPDE.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
-
DiffEqFlux.jl771Universal 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
-
DifferentialEquations.jl2503Multi-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.
-
Quaternionic.jl15Quaternions for Julia
-
ModelPredictiveControl.jl2A model predictive control package for Julia.
-
QuantumSavory.jl8-
Loading more...