SciML / ModelingToolkit.jl

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Prevent ODEProblem construction when not providing parameters

anandijain commented Mar 8, 2021

using ModelingToolkit, DifferentialEquations
@parameters r t
@variables x(t)
D = Differential(t)
eq = D(x) ~ r*x 
sys = ODESystem(eq)
prob = ODEProblem(sys, rand(1), (0, 10.))
solve(prob)

It makes much more sense to error when ODEProblem is not given adequate information to be able to solve, and not during solve.

Symbolic tspan

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Require systems kwarg to all system types' constructors to have unique names

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fredrik-johansson / mpmath

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Python library for arbitrary-precision floating-point arithmetic

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SciML / DiffEqFlux.jl

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Turn an example from robotics and control into a tutorial?

4

ChrisRackauckas commented Jan 13, 2021

https://arxiv.org/abs/2012.06684

@samuela is there code to share that could become a tutorial? I think it would be good to make one.

neural_ode_sciml example fails when Dense layer replaced by GRU

15

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Galerkin Neural ODEs

8

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Zymrael / awesome-neural-ode

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A collection of resources regarding the interplay between differential equations, dynamical systems, deep learning, control and optimization.

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Updated Apr 5, 2021

SciML / SciMLTutorials.jl

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Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

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SciML / NeuralPDE.jl

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Allow number of boundary points to be chosen by the user by a keyword argument

ChrisRackauckas commented Apr 17, 2021

The title should be fairly descriptive here.

QuasiRandomTraining, every training step take a new quasirandom sample

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PINNs Lorenz param estim : kink in the output path of the predicted variables

11

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SciML / diffeqpy

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Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

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SciML / OrdinaryDiffEq.jl

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High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

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titu1994 / tfdiffeq

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Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support

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Updated Aug 26, 2020
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SciML / Sundials.jl

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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

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shervinea / stanford-cme-102-ordinary-differential-equations

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VIP cheatsheets for Stanford's CME 102 Ordinary Differential Equations for Engineers

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SciML / DiffEqBase.jl

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The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

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SciML / DiffEqBayes.jl

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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

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Updated Apr 22, 2021
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SciML / diffeqr

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Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem

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PKU-NIP-Lab / BrainPy

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BrainPy: A general platform for computational neuroscience and brain-inspired computation

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bueler / p4pdes

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C and Python examples from my book on using PETSc to solve PDEs

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bjodah / pyodesys

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∫ Straightforward numerical integration of systems of ordinary differential equations

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planelles20 / numerical-methods-fortran

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Solving linear, nonlinear equations, ordinary differential equations, ... using numerical methods in fortran

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aseyboldt / sunode

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Solve ODEs fast, with support for PyMC3

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SciML / DiffEqPhysics.jl

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A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)

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SciML / DiffEqParamEstim.jl

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Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

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SciML / DASSL.jl

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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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