{"payload":{"pageCount":6,"repositories":[{"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)","topicNames":["high-performance","ordinary-differential-equations","adaptive","differentialequations","event-handling","hacktoberfest","julia","ode","differential-equations","scientific-machine-learning"],"topicsNotShown":1,"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":298,"starsCount":507,"forksCount":195,"license":"Other","participation":[29,47,18,41,3,6,7,8,12,16,8,23,14,2,0,5,2,3,5,9,0,0,10,16,27,8,9,0,44,7,21,37,11,5,13,14,19,12,2,27,9,3,5,1,14,8,18,30,18,18,47,49],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-24T02:16:58.835Z"}},{"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","topicNames":["computer-algebra","optimization","symbolic","dde","ordinary-differential-equations","sde","pde","dae","stochastic-differential-equations","delay-differential-equations"],"topicsNotShown":10,"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":62,"issueCount":279,"starsCount":1369,"forksCount":195,"license":"Other","participation":[21,16,19,5,8,7,11,3,2,9,3,8,11,15,16,27,31,23,17,27,12,9,14,15,15,4,5,12,16,28,17,19,5,9,15,31,24,40,48,120,59,32,26,45,29,24,32,6,12,4,5,1],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-24T02:04:33.337Z"}},{"type":"Public","name":"SciMLDocs","owner":"SciML","isFork":false,"description":"Global documentation for the Julia SciML Scientific Machine Learning Organization","topicNames":["documentation","julia","scientific-machine-learning","sciml"],"topicsNotShown":0,"allTopics":["documentation","julia","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":1,"issueCount":15,"starsCount":52,"forksCount":38,"license":"MIT License","participation":[0,0,2,1,5,0,0,0,0,2,6,0,0,12,6,1,15,4,4,3,0,0,0,19,5,1,0,0,0,2,8,4,2,2,0,4,4,2,6,2,10,0,0,0,6,3,0,2,0,1,2,21],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-24T01:56:15.772Z"}},{"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","topicNames":["automatic-differentiation","garbage-collection","high-performance-computing","differentiable-programming"],"topicsNotShown":0,"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":[0,0,0,8,0,0,0,0,0,0,0,0,0,0,2,0,2,2,0,0,0,0,0,0,0,0,0,8,5,2,4,21,7,0,0,0,7,11,4,0,2,0,0,0,0,2,4,0,4,3,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-24T01:26:15.739Z"}},{"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","topicNames":["machine-learning","deep-learning","julia","neural-networks","differential-equations","pde","pde-solver","scientific-machine-learning","sciml","feynman-kac"],"topicsNotShown":1,"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":69,"forksCount":11,"license":"Other","participation":[0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,2,3,0,11,0,0,4,0,0,0,0,0,0,0,0,0,3,9,15,37,17,19,2,0,0,0,0,0,0,2,0,0,0,2,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-24T01:24:51.058Z"}},{"type":"Public","name":"PubChem.jl","owner":"SciML","isFork":false,"description":"","topicNames":[],"topicsNotShown":0,"allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":0,"starsCount":7,"forksCount":2,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-24T00:04:03.394Z"}},{"type":"Public","name":"BaseModelica.jl","owner":"SciML","isFork":false,"description":"Importers for the BaseModelica standard into the Julia ModelingToolkit ecosystem","topicNames":["julia","ode","modelica","differential-equations","dae","sciml","symbolic-numeric-computing"],"topicsNotShown":0,"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":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6,4,22,2,5,6,0,2,0,0,0,5,2,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T21:46:34.358Z"}},{"type":"Public","name":"SciMLStructures.jl","owner":"SciML","isFork":false,"description":"A structure interface for SciML to give queryable properties from user data and parameters","topicNames":["api","interfaces"],"topicsNotShown":0,"allTopics":["api","interfaces"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":2,"issueCount":0,"starsCount":5,"forksCount":4,"license":"MIT License","participation":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,10,0,3,8,2,0,2,0,0,2,0,0,0,4,3,4,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T21:36:27.365Z"}},{"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.","topicNames":["simulation","biology","dsl","julia","systems-biology","ode","reaction-network","differential-equations","sde","chemical-reactions"],"topicsNotShown":5,"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":57,"issueCount":96,"starsCount":425,"forksCount":71,"license":"Other","participation":[0,0,0,0,6,16,8,15,9,15,8,11,5,3,19,16,36,17,25,1,6,12,41,52,102,37,0,65,3,20,6,37,1,0,15,30,14,11,0,1,2,4,35,23,64,45,4,3,0,20,42,67],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T21:16:43.173Z"}},{"type":"Public","name":"SciMLBenchmarks.jl","owner":"SciML","isFork":false,"description":"Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R","topicNames":["python","benchmark","ai","julia","matlab","pytorch","ode","partial-differential-equations","differential-equations","differentialequations"],"topicsNotShown":9,"allTopics":["python","benchmark","ai","julia","matlab","pytorch","ode","partial-differential-equations","differential-equations","differentialequations","sde","pde","dae","jax","neural-ode","scientific-machine-learning","nerual-differential-equations","sciml","ai-for-science"],"primaryLanguage":{"name":"MATLAB","color":"#e16737"},"pullRequestCount":52,"issueCount":34,"starsCount":296,"forksCount":73,"license":"MIT License","participation":[1,6,0,0,0,0,0,7,9,24,22,15,6,84,70,92,60,19,31,11,2,8,10,2,4,1,3,9,19,8,15,0,39,10,22,12,28,43,12,3,8,3,9,2,0,72,12,7,0,4,0,6],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T19:27:50.314Z"}},{"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.","topicNames":["ode","dde","differentialequations","sde","dae","sensitivity-analysis","hacktoberfest","adjoint","backpropogation","neural-ode"],"topicsNotShown":4,"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":5,"issueCount":86,"starsCount":316,"forksCount":66,"license":"Other","participation":[6,3,4,3,0,7,9,7,7,11,19,18,12,14,7,5,14,11,1,11,11,10,12,9,21,0,2,2,18,17,31,26,32,17,17,7,10,0,10,16,5,10,1,0,4,0,0,1,0,3,45,15],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T15:55:31.660Z"}},{"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","topicNames":["parameters","jacobian","differential-equations","scientific-machine-learning","sciml"],"topicsNotShown":0,"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":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,1,0,2,0,0,0,3,8,0,0,4,3,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T15:38:13.316Z"}},{"type":"Public","name":"SciMLBase.jl","owner":"SciML","isFork":false,"description":"The Base interface of the SciML ecosystem","topicNames":["julia","ode","dde","ordinary-differential-equations","differentialequations","sde","dae","scientific-machine-learning","sciml"],"topicsNotShown":0,"allTopics":["julia","ode","dde","ordinary-differential-equations","differentialequations","sde","dae","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":21,"issueCount":56,"starsCount":116,"forksCount":88,"license":"MIT License","participation":[5,20,8,4,3,0,18,7,6,10,6,16,4,6,19,21,52,27,38,27,9,14,17,26,4,5,5,5,32,31,22,28,32,13,12,13,25,31,10,35,9,6,6,16,5,5,24,12,25,9,13,9],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T06:55:48.193Z"}},{"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","topicNames":["dsl","indexing","symbolic","domain-specific-language","symbolic-computation","scientific-machine-learning","sciml","high-level-interfaces","acausal-modeling"],"topicsNotShown":0,"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":5,"starsCount":11,"forksCount":5,"license":"MIT License","participation":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,3,5,1,2,6,1,9,3,7,26,1,4,6,3,2,5,5,4,7,11,3,19,1,6,5,0,17,4,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T06:18:26.223Z"}},{"type":"Public","name":"ModelingToolkitNeuralNets.jl","owner":"SciML","isFork":false,"description":"Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)","topicNames":["machine-learning","julia","neural-networks","ude","neural-ode","sciml","symbolic-numerics"],"topicsNotShown":0,"allTopics":["machine-learning","julia","neural-networks","ude","neural-ode","sciml","symbolic-numerics"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":1,"starsCount":15,"forksCount":1,"license":"MIT License","participation":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,38,2,16,4,5,3,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T05:33:52.909Z"}},{"type":"Public","name":"CommonSolve.jl","owner":"SciML","isFork":false,"description":"A common solve function for scientific machine learning (SciML) and beyond","topicNames":["interface","composibility","scientific-machine-learning","sciml"],"topicsNotShown":0,"allTopics":["interface","composibility","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":0,"starsCount":16,"forksCount":8,"license":null,"participation":[0,8,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,2,2,0,0,4,5,0,0,0,5,2,2,2,2,0,0,0,2,0,0,0,4,2,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-23T04:06:28.892Z"}},{"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.","topicNames":["high-performance-computing","factorization","nonlinear-equations","sparse-matrix","sparse-matrices","newton-raphson","steady-state","bracketing","equilibrium","newton-method"],"topicsNotShown":6,"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":5,"issueCount":28,"starsCount":209,"forksCount":38,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-22T19:45:30.863Z"}},{"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","topicNames":["dde","partial-differential-equations","ordinary-differential-equations","differentialequations","sde","pde","dae","stochastic-differential-equations","delay-differential-equations","hacktoberfest"],"topicsNotShown":7,"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":13,"issueCount":55,"starsCount":300,"forksCount":106,"license":"Other","participation":[0,6,3,2,1,0,0,9,5,1,1,2,3,0,8,11,18,21,14,15,9,3,20,17,6,5,5,4,4,11,24,6,4,2,2,0,9,17,2,15,7,0,0,0,7,2,2,0,8,2,15,11],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-22T17:59:38.387Z"}},{"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.","topicNames":["automatic-differentiation","global-optimization","hacktoberfest","nonlinear-optimization","convex-optimization","algorithmic-differentiation","mixed-integer-programming","derivative-free-optimization","sciml","local-optimization"],"topicsNotShown":3,"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":2,"issueCount":71,"starsCount":680,"forksCount":72,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-22T15:32:14.855Z"}},{"type":"Public","name":"OptimizationBase.jl","owner":"SciML","isFork":false,"description":"The base package for Optimization.jl, containing the structs and basic functions for it.","topicNames":[],"topicsNotShown":0,"allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":6,"issueCount":14,"starsCount":7,"forksCount":2,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-22T13:27:51.168Z"}},{"type":"Public","name":"ModelingToolkitStandardLibrary.jl","owner":"SciML","isFork":false,"description":"A standard library of components to model the world and beyond","topicNames":["ode","scientific-computing","modelica","ordinary-differential-equations","sde","blockmodels","scientific-machine-learrning","acausal","julia","differential-equations"],"topicsNotShown":1,"allTopics":["ode","scientific-computing","modelica","ordinary-differential-equations","sde","blockmodels","scientific-machine-learrning","acausal","julia","differential-equations","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":20,"issueCount":33,"starsCount":99,"forksCount":35,"license":"MIT License","participation":[12,0,8,5,1,3,2,21,10,6,2,2,7,2,7,6,3,9,11,0,2,3,1,0,0,0,0,0,5,0,0,2,10,2,4,0,12,4,15,5,3,15,12,6,0,2,8,8,10,0,3,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-22T10:05:49.241Z"}},{"type":"Public","name":"NeuralLyapunov.jl","owner":"SciML","isFork":false,"description":"A library for searching for neural Lyapunov functions in Julia.","topicNames":[],"topicsNotShown":0,"allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":2,"starsCount":1,"forksCount":1,"license":"Other","participation":[1,0,0,0,0,0,6,0,0,0,14,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,7,1,10,32,1,24,6,0,15,10,10,0,0,0,0,2,4,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-21T18:58:46.669Z"}},{"type":"Public","name":"BoundaryValueDiffEq.jl","owner":"SciML","isFork":false,"description":"Boundary value problem (BVP) solvers for scientific machine learning (SciML)","topicNames":["gpu","bvp","neural-ode","scientific-machine-learning","neural-differential-equations","neural-bvp","sciml","differential-equations","differentialequations"],"topicsNotShown":0,"allTopics":["gpu","bvp","neural-ode","scientific-machine-learning","neural-differential-equations","neural-bvp","sciml","differential-equations","differentialequations"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":6,"issueCount":20,"starsCount":40,"forksCount":30,"license":"Other","participation":[4,10,7,2,6,6,1,3,7,6,16,13,7,10,19,7,7,11,14,23,14,10,31,45,4,0,0,0,3,10,10,0,3,0,2,4,3,4,4,3,0,0,7,15,2,0,0,0,3,2,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-21T15:02:52.138Z"}},{"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","topicNames":["vector","array","recursion","scientific-machine-learning","sciml"],"topicsNotShown":0,"allTopics":["vector","array","recursion","scientific-machine-learning","sciml"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":2,"issueCount":26,"starsCount":204,"forksCount":56,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-21T13:28:30.355Z"}},{"type":"Public","name":"SurrogatesBase.jl","owner":"SciML","isFork":false,"description":"Basically just a surrogate in disguise","topicNames":[],"topicsNotShown":0,"allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":0,"issueCount":0,"starsCount":3,"forksCount":3,"license":"MIT License","participation":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,2,1,0,0,0,0,0,0,2,0,2,1,0,0,0,3,0,1,0,0,0,0,0,17,0,0,0,0,0,8,0,0,2,3,0,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-21T13:03:08.798Z"}},{"type":"Public","name":"sciml.ai","owner":"SciML","isFork":false,"description":"The SciML Scientific Machine Learning Software Organization Website","topicNames":["machine-learning","julia","julia-language","ode","dde","sde","dae","julialang","franklin","neural-ode"],"topicsNotShown":4,"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":[3,0,0,0,2,0,3,1,0,0,0,0,0,1,1,0,1,0,0,0,0,2,1,3,0,1,0,0,0,0,0,0,0,0,8,0,0,2,0,0,0,7,2,0,0,0,4,0,10,6,9,1],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-21T11:13:00.069Z"}},{"type":"Public","name":"FastAlmostBandedMatrices.jl","owner":"SciML","isFork":false,"description":"","topicNames":[],"topicsNotShown":0,"allTopics":[],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":2,"issueCount":1,"starsCount":4,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-21T00:25:08.924Z"}},{"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.","topicNames":["gpu","distributed-computing","factorization","amg","multigrid","krylov-methods","linear-solvers","preconditioners","sciml","newton-krylov"],"topicsNotShown":4,"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":224,"forksCount":48,"license":"Other","participation":[58,35,12,12,0,5,0,10,0,13,28,3,9,6,0,2,11,33,26,7,10,19,47,40,5,0,2,14,9,9,11,2,8,0,5,0,4,36,4,15,3,0,0,9,0,2,1,12,0,0,8,0],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-21T00:12:16.086Z"}},{"type":"Public","name":"SimpleNonlinearSolve.jl","owner":"SciML","isFork":false,"description":"Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.","topicNames":["newton","julia","differential-equations","nonlinear-dynamics","newton-raphson","broyden-method","bisection-method","rootfinding","nonlinear-systems","scientific-machine-learning"],"topicsNotShown":4,"allTopics":["newton","julia","differential-equations","nonlinear-dynamics","newton-raphson","broyden-method","bisection-method","rootfinding","nonlinear-systems","scientific-machine-learning","sciml","falsi-position","falsi-method","nonlinear-solvers"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":4,"issueCount":2,"starsCount":61,"forksCount":19,"license":"MIT License","participation":[2,3,0,3,20,3,5,12,13,3,0,0,0,0,10,13,2,3,0,0,10,5,3,12,0,15,25,11,0,2,11,0,3,27,2,0,11,16,4,5,0,5,0,0,5,0,0,1,1,2,2,5],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-20T21:32:04.733Z"}},{"type":"Public","name":"DiffEqFlux.jl","owner":"SciML","isFork":false,"description":"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","topicNames":["neural-networks","partial-differential-equations","differential-equations","ordinary-differential-equations","differentialequations","stochastic-differential-equations","delay-differential-equations","pinn","neural-ode","scientific-machine-learning"],"topicsNotShown":10,"allTopics":["neural-networks","partial-differential-equations","differential-equations","ordinary-differential-equations","differentialequations","stochastic-differential-equations","delay-differential-equations","pinn","neural-ode","scientific-machine-learning","neural-sde","neural-pde","neural-dde","neural-differential-equations","stiff-ode","scientific-ml","scientific-ai","neural-jump-diffusions","neural-sdes","physics-informed-learning"],"primaryLanguage":{"name":"Julia","color":"#a270ba"},"pullRequestCount":2,"issueCount":39,"starsCount":843,"forksCount":151,"license":"MIT License","participation":[7,0,0,0,2,1,0,7,2,22,1,2,0,0,4,2,4,2,0,8,12,0,9,12,16,14,0,6,7,4,7,0,3,3,2,0,8,8,0,4,3,0,0,0,5,2,0,1,0,0,7,17],"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-20T20:27:02.303Z"}}],"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"}