The Base interface of the SciML ecosystem
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Updated
Jun 11, 2024 - Julia
The Base interface of the SciML ecosystem
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Fast and automatic structural identifiability software for ODE systems
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Advanced Multilanguage Interface to CVODES and IDAS
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
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
Arrays with arbitrarily nested named components.
Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
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.
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
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
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