ChrisRackauckas-Claude/Optimization.jl
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.
Beep Boop this is the ChrisRackauckas robot for SciML development
Repositories
ChrisRackauckas-Claude/Static.jl
Static types useful for dispatch and generated functions.
ChrisRackauckas-Claude/DynamicExpressions.jl
Ridiculously fast symbolic expressions
ChrisRackauckas-Claude/LLVM.jl
Julia wrapper for the LLVM C API
ChrisRackauckas-Claude/SciMLBenchmarksOutput
SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
ChrisRackauckas-Claude/Whisper.jl
Implementation of OpenAI Whisper model based on whisper.cpp
ChrisRackauckas-Claude/HostCPUFeatures.jl
ChrisRackauckas-Claude/StochasticAD.jl
Research package for automatic differentiation of programs containing discrete randomness.
ChrisRackauckas-Claude/Yggdrasil
Collection of builder repositories for BinaryBuilder.jl
ChrisRackauckas-Claude/TrustRegionLeastSquares.jl
ChrisRackauckas-Claude/MutableArithmetics.jl
Interface for arithmetics on mutable types in Julia
ChrisRackauckas-Claude/Symbolics.jl
Symbolic programming for the next generation of numerical software
ChrisRackauckas-Claude/NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
ChrisRackauckas-Claude/LineSearch.jl
Line Search Algorithms
ChrisRackauckas-Claude/SciMLBase.jl
The Base interface of the SciML ecosystem
ChrisRackauckas-Claude/ModelingToolkit.jl
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
ChrisRackauckas-Claude/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
ChrisRackauckas-Claude/EasyModelAnalysis.jl
High level functions for analyzing the output of simulations
ChrisRackauckas-Claude/JumpProcesses.jl
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
ChrisRackauckas-Claude/OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
ChrisRackauckas-Claude/Dedalus.jl
ChrisRackauckas-Claude/MonteCarloIntegration.jl
A package for multi-dimensional integration using monte carlo methods
ChrisRackauckas-Claude/AudioPlugins.jl
Headless hosting of CLAP and LV2 audio plugins from Julia, behind a C ABI of scalar doubles
ChrisRackauckas-Claude/SciMLBenchmarks.jl
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
ChrisRackauckas-Claude/SparseBandedMatrices.jl
ChrisRackauckas-Claude/Daedalus.jl
[UNDER DEVELOPMENT] Julia package translating the R package daedalus fo projecting health, social, and economic costs of a pandemic.
ChrisRackauckas-Claude/CacheAwareConv.jl
ChrisRackauckas-Claude/Enzyme
High-performance automatic differentiation of LLVM and MLIR.
ChrisRackauckas-Claude/ModelingToolkitStandardLibrary.jl
A standard library of components to model the world and beyond
ChrisRackauckas-Claude/SciMLSensitivity.jl
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.