{"payload":{"pageCount":6,"repositories":[{"type":"Public","name":"PubChem.jl","owner":"SciML","isFork":false,"description":"","allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":0,"starsCount":6,"forksCount":2,"license":"MIT License","participation":[35,1,0,9,1,1,0,0,0,0,0,22,7,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-02T00:04:19.394Z"}},{"type":"Public","name":"Catalyst.jl","owner":"SciML","isFork":false,"description":"Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.","allTopics":["simulation","biology","dsl","julia","systems-biology","ode","reaction-network","differential-equations","sde","chemical-reactions","gillespie-algorithm","systems-biology-simulation","rate-laws","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":36,"issueCount":96,"starsCount":427,"forksCount":71,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T22:01:06.465Z"}},{"type":"Public","name":"NonlinearSolve.jl","owner":"SciML","isFork":false,"description":"High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.","allTopics":["high-performance-computing","factorization","nonlinear-equations","sparse-matrix","sparse-matrices","newton-raphson","steady-state","bracketing","equilibrium","newton-method","newton-krylov","deep-equilibrium-models","julia","differential-equations","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":6,"issueCount":29,"starsCount":214,"forksCount":38,"license":"MIT License","participation":[4,0,11,3,0,0,2,5,6,0,11,7,2,3,31,31,23,22,23,74,55,40,20,12,11,18,58,28,46,45,24,22,21,3,9,19,10,18,3,6,0,4,4,8,2,10,7,7,2,17,22,7],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T18:13:03.257Z"}},{"type":"Public","name":"DataInterpolations.jl","owner":"SciML","isFork":false,"description":"A library of data interpolation and smoothing functions","allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":3,"issueCount":17,"starsCount":194,"forksCount":40,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T15:48:47.802Z"}},{"type":"Public","name":"RuntimeGeneratedFunctions.jl","owner":"SciML","isFork":false,"description":"Functions generated at runtime without world-age issues or overhead","allTopics":["julia"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":11,"starsCount":99,"forksCount":14,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T14:53:21.296Z"}},{"type":"Public","name":"PoissonRandom.jl","owner":"SciML","isFork":false,"description":"Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)","allTopics":["high-performance-computing","poisson","poisson-distribution","poisson-processes","scientific-machine-learning","sciml","tau-leaping","julia"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":1,"starsCount":15,"forksCount":6,"license":"Other","participation":[0,0,6,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,6,2,0,4,0,6,0,0,0,5,0,4,2,0,0,0,0,2,0,0,0,4,3,0,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T12:40:19.231Z"}},{"type":"Public","name":"EllipsisNotation.jl","owner":"SciML","isFork":false,"description":"Julia-based implementation of ellipsis array indexing notation `..`","allTopics":["arrays","julia","julia-language","julialang"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":2,"starsCount":87,"forksCount":14,"license":"Other","participation":[0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,1,0,0,0,5,2,2,2,1,5,0,0,0,5,0,4,2,0,0,0,0,0,2,0,0,5,2,0,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T12:38:20.432Z"}},{"type":"Public","name":"SciMLSensitivity.jl","owner":"SciML","isFork":false,"description":"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, SDEs, DDEs, DAEs, etc.","allTopics":["ode","dde","differentialequations","sde","dae","sensitivity-analysis","hacktoberfest","adjoint","backpropogation","neural-ode","scientific-machine-learning","neural-sde","sciml","differential-equations"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":7,"issueCount":86,"starsCount":316,"forksCount":67,"license":"Other","participation":[3,6,0,0,12,10,2,6,11,27,10,15,12,8,6,17,6,4,8,15,11,7,12,17,0,2,4,16,17,38,26,26,16,19,9,6,0,12,16,6,7,1,2,2,0,1,0,1,6,43,17,7],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T10:06:43.185Z"}},{"type":"Public","name":"GlobalSensitivity.jl","owner":"SciML","isFork":false,"description":"Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia","allTopics":["ode","morris-method","ordinary-differential-equations","sensitivity-analysis","global-sensitivity-analysis","gsa","sobol-method","efast","julia","julia-language","differential-equations","julialang","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":2,"issueCount":23,"starsCount":49,"forksCount":21,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T09:24:18.165Z"}},{"type":"Public","name":"SciMLDocs","owner":"SciML","isFork":false,"description":"Global documentation for the Julia SciML Scientific Machine Learning Organization","allTopics":["documentation","julia","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":1,"issueCount":15,"starsCount":52,"forksCount":38,"license":"MIT License","participation":[0,3,0,5,0,0,0,0,2,6,0,4,10,5,2,15,2,7,0,0,0,0,19,6,0,0,0,0,2,12,0,2,2,0,4,5,4,5,3,7,0,0,6,0,3,2,0,0,3,5,16,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T02:12:00.772Z"}},{"type":"Public","name":"DiffEqBase.jl","owner":"SciML","isFork":false,"description":"The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems","allTopics":["dde","partial-differential-equations","ordinary-differential-equations","differentialequations","sde","pde","dae","stochastic-differential-equations","delay-differential-equations","hacktoberfest","differential-algebraic-equations","neural-ode","neural-differential-equations","ode","differential-equations","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":15,"issueCount":54,"starsCount":299,"forksCount":106,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-01T01:05:31.495Z"}},{"type":"Public","name":"NeuralLyapunov.jl","owner":"SciML","isFork":false,"description":"A library for searching for neural Lyapunov functions in Julia.","allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":2,"starsCount":1,"forksCount":1,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T22:37:44.669Z"}},{"type":"Public","name":"LinearSolve.jl","owner":"SciML","isFork":false,"description":"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.","allTopics":["gpu","distributed-computing","factorization","amg","multigrid","krylov-methods","linear-solvers","preconditioners","sciml","newton-krylov","julia","linear-algebra","differential-equations","scientific-machine-learning"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":10,"issueCount":61,"starsCount":227,"forksCount":50,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T22:30:50.570Z"}},{"type":"Public","name":"OrdinaryDiffEq.jl","owner":"SciML","isFork":false,"description":"High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)","allTopics":["high-performance","ordinary-differential-equations","adaptive","differentialequations","event-handling","hacktoberfest","julia","ode","differential-equations","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":32,"issueCount":294,"starsCount":512,"forksCount":195,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T21:55:58.905Z"}},{"type":"Public","name":"ExponentialUtilities.jl","owner":"SciML","isFork":false,"description":"Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications (\"expmv\"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.","allTopics":["gpu","high-performance","exponential","krylov","krylov-methods","krylov-subspace-methods","matrix-exponential","expokit","matrix-exponentials","expmv","kiops","julia","differential-equations","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":4,"issueCount":22,"starsCount":93,"forksCount":29,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T21:46:58.389Z"}},{"type":"Public","name":"Optimization.jl","owner":"SciML","isFork":false,"description":"Mathematical 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.","allTopics":["automatic-differentiation","global-optimization","hacktoberfest","nonlinear-optimization","convex-optimization","algorithmic-differentiation","mixed-integer-programming","derivative-free-optimization","sciml","local-optimization","optimization","julia","scientific-machine-learning"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":5,"issueCount":72,"starsCount":681,"forksCount":75,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T20:42:21.257Z"}},{"type":"Public","name":"OptimizationBase.jl","owner":"SciML","isFork":false,"description":"The base package for Optimization.jl, containing the structs and basic functions for it.","allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":6,"issueCount":13,"starsCount":8,"forksCount":4,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T20:30:22.234Z"}},{"type":"Public","name":"SymbolicIndexingInterface.jl","owner":"SciML","isFork":false,"description":"A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages","allTopics":["dsl","indexing","symbolic","domain-specific-language","symbolic-computation","scientific-machine-learning","sciml","high-level-interfaces","acausal-modeling"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":7,"issueCount":6,"starsCount":11,"forksCount":6,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T17:36:22.424Z"}},{"type":"Public","name":"RecursiveArrayTools.jl","owner":"SciML","isFork":false,"description":"Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications","allTopics":["vector","array","recursion","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":2,"issueCount":26,"starsCount":205,"forksCount":56,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T17:20:57.293Z"}},{"type":"Public","name":"SciMLBase.jl","owner":"SciML","isFork":false,"description":"The Base interface of the SciML ecosystem","allTopics":["julia","ode","dde","ordinary-differential-equations","differentialequations","sde","dae","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":22,"issueCount":56,"starsCount":117,"forksCount":89,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T17:20:03.861Z"}},{"type":"Public","name":"ModelingToolkit.jl","owner":"SciML","isFork":false,"description":"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","allTopics":["computer-algebra","optimization","symbolic","dde","ordinary-differential-equations","sde","pde","dae","stochastic-differential-equations","delay-differential-equations","symbolic-computation","nonlinear-programming","equation-based","symbolic-numerics","acausal","julia","ode","differential-equations","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":66,"issueCount":280,"starsCount":1374,"forksCount":195,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T17:19:22.730Z"}},{"type":"Public","name":"FindFirstFunctions.jl","owner":"SciML","isFork":false,"description":"Faster `findfirst(==(val), dense_vector)`.","allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":1,"issueCount":2,"starsCount":6,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T13:39:10.574Z"}},{"type":"Public","name":"CellMLToolkit.jl","owner":"SciML","isFork":false,"description":"CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.","allTopics":["systems-biology","physiology","cellml","julia","ode","differential-equations","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":1,"issueCount":9,"starsCount":54,"forksCount":14,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T08:57:14.961Z"}},{"type":"Public","name":"sciml.ai","owner":"SciML","isFork":false,"description":"The SciML Scientific Machine Learning Software Organization Website","allTopics":["machine-learning","julia","julia-language","ode","dde","sde","dae","julialang","franklin","neural-ode","physics-informed-learning","differential-equations","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"CSS","color":"#563d7c"},"pullRequestCount":1,"issueCount":6,"starsCount":52,"forksCount":34,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T07:00:31.069Z"}},{"type":"Public","name":"BoundaryValueDiffEq.jl","owner":"SciML","isFork":false,"description":"Boundary value problem (BVP) solvers for scientific machine learning (SciML)","allTopics":["gpu","bvp","neural-ode","scientific-machine-learning","neural-differential-equations","neural-bvp","sciml","differential-equations","differentialequations"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":5,"issueCount":20,"starsCount":41,"forksCount":31,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T04:51:12.952Z"}},{"type":"Public","name":"PreallocationTools.jl","owner":"SciML","isFork":false,"description":"Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes","allTopics":["automatic-differentiation","garbage-collection","high-performance-computing","differentiable-programming"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":4,"starsCount":109,"forksCount":12,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T01:26:55.739Z"}},{"type":"Public","name":"HighDimPDE.jl","owner":"SciML","isFork":false,"description":"A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality","allTopics":["machine-learning","deep-learning","julia","neural-networks","differential-equations","pde","pde-solver","scientific-machine-learning","sciml","feynman-kac","deepbsde"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":5,"issueCount":6,"starsCount":70,"forksCount":11,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-31T01:24:51.058Z"}},{"type":"Public","name":"BaseModelica.jl","owner":"SciML","isFork":false,"description":"Importers for the BaseModelica standard into the Julia ModelingToolkit ecosystem","allTopics":["julia","ode","modelica","differential-equations","dae","sciml","symbolic-numeric-computing"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":1,"starsCount":2,"forksCount":3,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-30T21:44:59.358Z"}},{"type":"Public","name":"SciMLStructures.jl","owner":"SciML","isFork":false,"description":"A structure interface for SciML to give queryable properties from user data and parameters","allTopics":["api","interfaces"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":2,"issueCount":0,"starsCount":5,"forksCount":4,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-30T21:35:51.262Z"}},{"type":"Public","name":"ParameterizedFunctions.jl","owner":"SciML","isFork":false,"description":"A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications","allTopics":["parameters","jacobian","differential-equations","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":1,"issueCount":1,"starsCount":77,"forksCount":18,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-30T15:37:55.316Z"}}],"repositoryCount":170,"userInfo":null,"searchable":true,"definitions":[],"typeFilters":[{"id":"all","text":"All"},{"id":"public","text":"Public"},{"id":"source","text":"Sources"},{"id":"fork","text":"Forks"},{"id":"archived","text":"Archived"},{"id":"template","text":"Templates"}],"compactMode":false},"title":"Repositories"}