Direct and Inverse Solver for Kinetic Capillary Electrophoresis (KCE)
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Updated
Jul 7, 2017 - MATLAB
Direct and Inverse Solver for Kinetic Capillary Electrophoresis (KCE)
Lecuture notes on practical methods for ordinary differential equations
Provides a solution for any resolvable differential equation with a degree n>1, using Euler or RK4 methods.
Simple program that solves specified differential equation using finite element method, written in Python
Euler's Method in Python to approximate solution of IVPs (differential equations)
Solvers for finite element discretizations of PDEs in the SciML scientific machine learning ecosystem
Monte Carlo simulation routines for high-performance parallelization of differential equation solvers and scientific machine learning
Interface to DASKR, a differential algebraic system solver for the SciML scientific machine learning ecosystem
A general purpose numerical simulator supporting nested dynamical systems and a convenient macro-based data logger.
Differential equation problem specifications and scientific machine learning for common financial models
Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF) in the SciML scientific machine learning organization
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
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
Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.
Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language
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