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SciML
132 indexed repositories · View on GitHub
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DifferentialEquations.jl
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary ...
Julia · 3157 stars
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SciMLBook
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
HTML · 2044 stars
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ModelingToolkit.jl
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for ...
Julia · 1682 stars
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NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated s...
Julia · 1228 stars
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DiffEqFlux.jl
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learnin...
Julia · 926 stars
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Optimization.jl
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonl...
Julia · 839 stars
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OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differ...
Julia · 683 stars
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diffeqpy
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Python · 611 stars
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Catalyst.jl
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and ...
Julia · 526 stars
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BlackBoxOptim.jl
Black-box optimization for Julia
Julia · 464 stars
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DataDrivenDiffEq.jl
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
Julia · 430 stars
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SciMLSensitivity.jl
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, ...
Julia · 396 stars
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Surrogates.jl
Surrogate modeling and optimization for scientific machine learning (SciML)
Julia · 382 stars
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ComponentArrays.jl
Arrays with arbitrarily nested named components.
Julia · 375 stars
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Evolutionary.jl
Evolutionary & genetic algorithms for Julia
Julia · 352 stars
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SciMLBenchmarks.jl
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax),...
Julia · 346 stars
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DiffEqDocs.jl
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Julia · 328 stars
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DiffEqGPU.jl
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Julia · 327 stars
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NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity ...
Julia · 309 stars
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LinearSolve.jl
LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, a...
Julia · 293 stars
- 112 more repositories live on View on GitHub
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Difficulty 4/5 3-5 days Newbie friendliness 72/100
SciML/LinearSolve.jl#1328 ·
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A shift applied to a whole array variable in a clock partition refers to an undeclared variable Open
Difficulty 4/5 3-5 days Newbie friendliness 52/100
SciML/ModelingToolkit.jl#5169 ·
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SciML/ModelingToolkit.jl#5166 ·
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bug
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Difficulty 3/5 1-2 days Newbie friendliness 82/100
SciML/LinearSolve.jl#1322 ·
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Difficulty 3/5 1-2 days Newbie friendliness 74/100
SciML/LinearSolve.jl#1321 ·
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bot-generated enhancement needs-chris-review
Difficulty 5/5 Over a week Newbie friendliness 30/100
SciML/AudioPlugins.jl#58 ·
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Difficulty 2/5 1-2 days Newbie friendliness 74/100
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Difficulty 4/5 3-5 days Newbie friendliness 52/100
SciML/LinearSolve.jl#1315 · 1 comment ·
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Difficulty 4/5 3-5 days Newbie friendliness 55/100
SciML/NonlinearSolve.jl#1288 ·
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Most in-place factorizations ignore `alias_A`, destroying the caller's matrix on refactorization Open
Difficulty 4/5 3-5 days Newbie friendliness 68/100
SciML/LinearSolve.jl#1313 · 1 comment ·
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Difficulty 5/5 Over a week Newbie friendliness 38/100
SciML/LinearSolve.jl#1309 ·
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SciML/LinearSolve.jl#1306 ·
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Difficulty 5/5 Over a week Newbie friendliness 38/100
SciML/NonlinearSolve.jl#1283 ·
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Difficulty 4/5 3-5 days Newbie friendliness 48/100
SciML/NeuralPDE.jl#1181 ·
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Difficulty 3/5 1-2 days Newbie friendliness 68/100
SciML/PDEBase.jl#122 ·
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Difficulty 4/5 3-5 days Newbie friendliness 58/100
SciML/ModelingToolkit.jl#5151 ·
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Difficulty 2/5 1-3 hours Newbie friendliness 78/100
SciML/NeuralPDE.jl#1180 ·
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Difficulty 4/5 3-5 days Newbie friendliness 45/100
SciML/ModelingToolkit.jl#5150 ·
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Difficulty 3/5 1-2 days Newbie friendliness 74/100
SciML/NeuralPDE.jl#1179 ·
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Sampling lifecycle: resample! caveats, seeded variant, and RNG consumption by symbolic_discretize Open
Difficulty 4/5 3-5 days Newbie friendliness 48/100
SciML/NeuralPDE.jl#1178 ·
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Difficulty 5/5 Over a week Newbie friendliness 45/100
SciML/NeuralPDE.jl#1176 ·
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Difficulty 5/5 Over a week Newbie friendliness 48/100
SciML/NeuralPDE.jl#1177 ·
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Difficulty 5/5 Over a week Newbie friendliness 35/100
SciML/NeuralPDE.jl#1175 ·
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Difficulty 4/5 3-5 days Newbie friendliness 55/100
SciML/NeuralPDE.jl#1174 ·
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Difficulty 4/5 3-5 days Newbie friendliness 55/100
SciML/NeuralPDE.jl#1172 ·
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Difficulty 4/5 3-5 days Newbie friendliness 48/100
SciML/NeuralPDE.jl#1173 ·
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Difficulty 5/5 Over a week Newbie friendliness 25/100
SciML/NeuralPDE.jl#1171 ·
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Difficulty 5/5 Over a week Newbie friendliness 35/100
SciML/NeuralPDE.jl#1170 ·
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Difficulty 5/5 Over a week Newbie friendliness 35/100
SciML/NeuralPDE.jl#1169 ·
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Difficulty 3/5 1-2 days Newbie friendliness 72/100
SciML/ModelingToolkit.jl#5147 ·
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Difficulty 4/5 3-5 days Newbie friendliness 55/100
SciML/MethodOfLines.jl#697 ·
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Difficulty 4/5 3-5 days Newbie friendliness 58/100
SciML/MethodOfLines.jl#696 ·
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Difficulty 4/5 3-5 days Newbie friendliness 58/100
SciML/SciMLSensitivity.jl#1663 ·
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Difficulty 5/5 Over a week Newbie friendliness 38/100
SciML/NonlinearSolve.jl#1274 ·
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Difficulty 3/5 1-2 days Newbie friendliness 74/100
SciML/SciMLSensitivity.jl#1660 ·
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bug
Difficulty 3/5 1-2 days Newbie friendliness 64/100
SciML/Optimization.jl#1358 · 2 comments ·
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Difficulty 4/5 3-5 days Newbie friendliness 52/100
SciML/NonlinearSolve.jl#1270 ·
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Difficulty 5/5 Over a week Newbie friendliness 35/100
SciML/ModelingToolkit.jl#5140 ·
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Compile and generate code for whole-array equation models without scalarizing per element (tracking) Openbot-generated needs-chris-review
Difficulty 5/5 Over a week Newbie friendliness 25/100
SciML/ModelingToolkit.jl#5139 · 1 comment ·
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Difficulty 3/5 1-2 days Newbie friendliness 76/100
SciML/Catalyst.jl#1561 ·
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bot-generated needs-chris-review
Difficulty 4/5 3-5 days Newbie friendliness 35/100
SciML/ModelingToolkit.jl#5138 ·
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Keep discretized dependent variables as array-valued unknowns instead of scalarizing per grid point Open
Difficulty 5/5 Over a week Newbie friendliness 35/100
SciML/MethodOfLines.jl#695 · 1 comment ·
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Difficulty 4/5 3-5 days Newbie friendliness 35/100
SciML/DiffEqGPU.jl#540 ·
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Difficulty 4/5 3-5 days Newbie friendliness 38/100
SciML/Dedalus.jl#15 ·
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Difficulty 4/5 3-5 days Newbie friendliness 35/100
SciML/ModelingToolkit.jl#5125 · 1 comment ·
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Difficulty 5/5 Over a week Newbie friendliness 20/100
SciML/NeuralPDE.jl#1166 ·
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bot-generated needs-chris-review
Difficulty 4/5 3-5 days Newbie friendliness 30/100
SciML/ModelingToolkit.jl#5119 ·
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Difficulty 2/5 1-3 hours Newbie friendliness 78/100
SciML/ModelingToolkit.jl#5118 ·
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Coverage sweep: operations tracing emits that the rule table does not recognize (25 gaps, 3 unsound) Open
Difficulty 5/5 Over a week Newbie friendliness 42/100
SciML/SymbolicAnalysis.jl#156 · 3 comments ·
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