Topic: ode Goto Github
Some thing interesting about ode
Some thing interesting about ode
ode,Advanced Multilanguage Interface to CVODES and IDAS
Organization: amici-dev
Home Page: https://amici.readthedocs.io/
ode,PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks
Organization: analysiscenter
ode,AUTO is a publicly available software for continuation and bifurcation problems in ordinary differential equations originally written in 1980 and widely used in the dynamical systems community.
Organization: auto-07p
ode,C++ library for ODE integration via Taylor's method and LLVM
User: bluescarni
Home Page: https://bluescarni.github.io/heyoka/
ode,Python library for ODE integration via Taylor's method and LLVM
User: bluescarni
ode,Fortran Object-Oriented Differential-equations Integration Environment, FOODIE
Organization: fortran-foss-programmers
ode,Code for the paper "Learning Differential Equations that are Easy to Solve"
User: jacobjinkelly
Home Page: https://arxiv.org/abs/2007.04504
ode,Fixed and variable-step Runge-Kutta solvers in Modern Fortran
User: jacobwilliams
Home Page: https://jacobwilliams.github.io/rklib/
ode,🏆 A weekly updated ranked list of popular open-source libraries and tools for Power System Analysis.
User: jinningwang
ode,Computing reachable states of dynamical systems in Julia
Organization: juliareach
Home Page: https://juliareach.github.io/ReachabilityAnalysis.jl/
ode,Convert julia objects to LaTeX equations, arrays or other environments.
User: korsbo
ode,Physics-Informed Neural networks for Advanced modeling
Organization: mathlab
Home Page: https://mathlab.github.io/PINA/
ode,Simulate from ODE-based population PK/PD and QSP models in R
Organization: metrumresearchgroup
Home Page: https://mrgsolve.org
ode,Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing
User: nathanaelbosch
ode,A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
Organization: neurodiffgym
Home Page: http://pypi.org/project/neurodiffeq/
ode,Code for Tensorflow Machine Learning Cookbook
User: nfmcclure
ode,Solving linear, nonlinear equations, ordinary differential equations, ... using numerical methods in fortran
User: planelles20
ode,Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
User: samholt
Home Page: https://samholt.github.io/NeuralLaplace/
ode,Solve Fractional Differential Equations using high performance numerical methods
Organization: scifracx
Home Page: https://scifracx.github.io/FractionalDiffEq.jl/dev/
ode,Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
Organization: sciml
Home Page: https://docs.sciml.ai/Catalyst/stable/
ode,Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
Organization: sciml
Home Page: https://docs.sciml.ai/DataDrivenDiffEq/stable/
ode,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.
Organization: sciml
ode,The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Organization: sciml
ode,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
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqBayes/stable/
ode,A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqCallbacks/stable/
ode,Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqDocs/stable/
ode,GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqGPU/stable/
ode,Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqParamEstim/stable/
ode,A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
Organization: sciml
ode,Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Organization: sciml
ode,Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem
Organization: sciml
ode,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.
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqDocs/stable/
ode,Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
Organization: sciml
Home Page: https://docs.sciml.ai/JumpProcesses/stable/
ode,An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Organization: sciml
Home Page: https://mtk.sciml.ai/dev/
ode,A standard library of components to model the world and beyond
Organization: sciml
Home Page: https://docs.sciml.ai/ModelingToolkitStandardLibrary/stable/
ode,A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
Organization: sciml
Home Page: https://docs.sciml.ai/MultiScaleArrays/stable/
ode,Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Organization: sciml
Home Page: https://docs.sciml.ai/NeuralPDE/stable/
ode,Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.
Organization: sciml
ode,High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Organization: sciml
Home Page: https://diffeq.sciml.ai/latest/
ode,The Base interface of the SciML ecosystem
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLBase/stable
ode,Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLBenchmarksOutput/stable/
ode,Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLExpectations/stable/
ode,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.
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLSensitivity/stable/
ode,A style guide for stylish Julia developers
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLStyle/stable/
ode,Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Organization: sciml
Home Page: https://tutorials.sciml.ai
ode,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
Organization: sciml
Home Page: https://diffeq.sciml.ai
ode,A collection of numerical methods written in Nim
Organization: scinim
Home Page: https://scinim.github.io/numericalnim/
ode,Java 3D Physics Engine & Library
User: tzaeschke
ode,Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.
Organization: willcox-research-group
Home Page: https://willcox-research-group.github.io/rom-operator-inference-Python3
ode,A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
User: zymrael
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