There are 1 repository under differentialequations topic.
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
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.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Arrays with arbitrarily nested named components.
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner
The Base interface of the SciML ecosystem
Fast and automatic structural identifiability software for ODE systems
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
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)
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
Differential equation problem specifications and scientific machine learning for common financial models
A general purpose numerical simulator supporting nested dynamical systems and a convenient macro-based data logger.
Monte Carlo simulation routines for high-performance parallelization of differential equation solvers and scientific machine learning
Solvers for finite element discretizations of PDEs in the SciML scientific machine learning ecosystem
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)
Provides a solution for any resolvable differential equation with a degree n>1, using Euler or RK4 methods.
Lecuture notes on practical methods for ordinary differential equations