Dependency Packages
-
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.
-
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
-
DSGE.jl798Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
-
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
-
NeuralPDE.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
-
NeuralNetDiffEq.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
-
DiffEqTutorials.jl694Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
-
QuantumOptics.jl459Library for the numerical simulation of closed as well as open quantum systems.
-
DataDrivenDiffEq.jl372Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
-
Catalyst.jl342Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software
-
Modia.jl298Modeling and simulation of multidomain engineering systems
-
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.
-
StochasticDiffEq.jl200Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
-
TuringModels.jl153Implementations of the models from the Statistical Rethinking book with Turing.jl
-
MethodOfLines.jl118Automatic Finite Difference PDE solving with Julia SciML
-
DiffEqBayes.jl117Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
-
FunctionalModels.jl111Equation-based modeling and simulations in Julia
-
Causal.jl102Causal.jl - A modeling and simulation framework adopting causal modeling approach.
-
ODEFilters.jl100Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing
-
SymbolicNumericIntegration.jl93SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
-
PowerDynamics.jl88Package for dynamical modeling of power grids
-
ValidatedNumerics.jl81Rigorous floating-point calculations with interval arithmetic in Julia
-
ModelingToolkitStandardLibrary.jl76A standard library of components to model the world and beyond
-
SemanticModels.jl76A julia package for representing and manipulating model semantics
-
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.
-
EasyModelAnalysis.jl74High level functions for analyzing the output of simulations
-
ParameterizedFunctions.jl72A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
-
Modia3D.jl64Modeling and Simulation of 3D systems
-
MultiScaleArrays.jl64A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
-
StructuralIdentifiability.jl63Fast and automatic structural identifiability software for ODE systems
-
IonSim.jl62A simple tool for simulating trapped ion systems
-
IntervalConstraintProgramming.jl60Calculate rigorously the feasible region for a set of real-valued inequalities with Julia
-
FundamentalsNumericalComputation.jl60Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.
-
FMI.jl59FMI.jl is a free-to-use software library for the Julia programming language which integrates FMI (fmi-standard.org): load or create, parameterize, differentiate and simulate FMUs seamlessly inside the Julia programming language!
-
AtomicGraphNets.jl58Atomic graph models for molecules and crystals in Julia
-
ReactionMechanismSimulator.jl54The amazing Reaction Mechanism Simulator for simulating large chemical kinetic mechanisms
-
QuantumCumulants.jl52Generalized mean-field equations in open quantum systems
-
CellMLToolkit.jl51CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.
-
Conductor.jl47Choo-choo
Loading more...