Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration.
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Updated
May 6, 2024 - Julia
Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration.
Julia package for Gaussian quadrature
adaptive 1d numerical Gauss–Kronrod integration in Julia
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
pure-Julia multidimensional h-adaptive integration
One- and multi-dimensional adaptive integration routines for the Julia language
Library for multidimensional numerical integration with four independent algorithms: Vegas, Suave, Divonne, and Cuhre.
A package for multi-dimensional integration using monte carlo methods
Julia package to easily calculate multidimensional integrals on discrete domains, brings MATLAB&Numpy trapz function to Julia
Numerical integration methods for structural dynamics problems
[DEPRECATED] Statistics & Numerical algorithms implemented in Julia.
Adaptive P/ODE numerics with Grassmann element TensorField assembly
Numerical integration of functions over Meshes.jl geometries
FEMQuad.jl package contains various of integration schemes for cartesian and tetrahedral domains. The most common integration rules are tabulated and focus is on speed. Each rule has own "label" so we can easily implement several rules with same degree. API is very simple making is easy to utilize package in different FEM projects.
A calculator of surface integrals for given surfaces and vector fields.
Bayesian Integration of functions
A toolbox for solving integral-related problems on (IFS-based) fractals.
A Julia package to numerically approximate 1d function integrals using any of a variety of different methods.
Adaptive quadratures for nonlinear approximation of low-dimensional PDEs using smooth neural networks
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