Hendrik Ranocha's Projects
Julia IEEE decimal floating-point via the Intel decimal-float library
Pure Julia decimal arithmetic library.
A deprecated way of handling discrete data in continuous equations
Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.
DiffEq solvers for delay differential equations
Material for a course on differential equations in machine learning
Interface for mathematical/statistical densities in Julia
Wrappers for calling the R deSolve differential equation solvers from Julia for scientific machine learning (SciML)
The tools for proper interactions between ApproxFun.jl and DifferentialEquations.jl for pseudospectiral partial differential equation discretizations in scientific machine learning (SciML)
The lightweight Base library for shared types and functionality.
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
Server behind the benchmarking bot @DiffEqBot for benchmarking scientific machine learning (SciML)
A library of useful callbacks for DiffEq solvers
Developer documentation for the DiffEq ecosystem
Benchmarking, testing, and development tools for differential equations
Diagrams and visualizations for scientific machine learning (SciML)
Documentation for the DiffEq ecosystem
A domain-specific language for specifying various differential equations
Differential equation problem specifications and scientific machine learning for common financial models
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
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Saving and loading of JuliaDiffEq types for I/O of scientific machine learning (SciML)
Monte Carlo simulation routines for high-performance parallelization of differential equation solvers and scientific machine learning
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
Library for common tools for solving PDEs with finite difference methods (FDM), finite volume methods (FVM), finite element methods (FEM), and psuedospectral methods in a way that integrates with the SciML Scientific Mechine Learning ecosystem
A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
A helper repository for diffeqpy to enable high-performance differential equation solving scientific machine learning (SciML) in Python