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Cogito Ergo Sum's Projects

diffeqbayes.jl icon diffeqbayes.jl

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

diffeqcallbacks.jl icon diffeqcallbacks.jl

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

diffeqdevdocs.jl icon diffeqdevdocs.jl

Developer documentation for the SciML scientific machine learning ecosystem's differential equation solvers

diffeqdevmaterials icon diffeqdevmaterials

Various developer materials, like PDFs, notes, derivations, etc. for differential equations and scientific machine learning (SciML)

diffeqdevtools.jl icon diffeqdevtools.jl

Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)

diffeqdocs.jl icon diffeqdocs.jl

Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem

diffeqfinancial.jl icon diffeqfinancial.jl

Differential equation problem specifications and scientific machine learning for common financial models

diffeqflux.jl icon diffeqflux.jl

Universal 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

diffeqgpu.jl icon diffeqgpu.jl

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

diffeqio.jl icon diffeqio.jl

Saving and loading of JuliaDiffEq types for I/O of scientific machine learning (SciML)

diffeqjump.jl icon diffeqjump.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)

diffeqmontecarlo.jl icon diffeqmontecarlo.jl

Monte Carlo simulation routines for high-performance parallelization of differential equation solvers and scientific machine learning

diffeqnoiseprocess.jl icon diffeqnoiseprocess.jl

A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

diffeqonline icon diffeqonline

It's Angular2 business in the front, and a Julia party in the back! It's scientific machine learning (SciML) for the web

diffeqoperators.jl icon diffeqoperators.jl

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

diffeqparamestim.jl icon diffeqparamestim.jl

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

diffeqphysics.jl icon diffeqphysics.jl

A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)

diffeqproblemlibrary.jl icon diffeqproblemlibrary.jl

A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools

diffeqpy icon diffeqpy

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

diffeqpy.jl icon diffeqpy.jl

A helper repository for diffeqpy to enable high-performance differential equation solving scientific machine learning (SciML) in Python

diffeqr icon diffeqr

Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem

diffeqsensitivity.jl icon diffeqsensitivity.jl

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.

diffequncertainty.jl icon diffequncertainty.jl

Uncertainty quantification for scientific machine learning (SciML) and differential equations

differencelists.jl icon differencelists.jl

A difference list implementation for Julia. These are a highly efficient and simple way to build up lists from parts: appending, prepending, and concatenation are all very fast (constant time). They're also immutable, so you can reuse the parts.

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