SciMLTutorials.jl
DiffEqSensitivity.jl
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| SciMLTutorials.jl | DiffEqSensitivity.jl | |
|---|---|---|
| 1 | 2 | |
| 707 | 184 | |
| 0.3% | - | |
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| 8 months ago | almost 2 years ago | |
| CSS | Julia | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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SciMLTutorials.jl
DiffEqSensitivity.jl
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[R] New directions in Neural Differential Equations
One reason is that it's not robust and has some odd counter example cases that can come up where the ODE solver is able to converge rapidly on the original problem but not so rapidly in the integral sense on the derivative values. One such case showed up in this issue, which was the impetus for the change in the forward-mode sense, while the reverse sense was changed in testing with direct quadratures (which will be mentioned in a bit).
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Odd Behavior: Neural network hybrid differential equation example
Thanks for letting us know. The fix is in https://github.com/SciML/DiffEqSensitivity.jl/pull/386 and hopefully that'll get released today.
What are some alternatives?
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
SciMLSensitivity.jl - 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.
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
auto-07p - 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.
DifferentialEquations.jl - 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.
18337 - 18.337 - Parallel Computing and Scientific Machine Learning
SciMLBook - Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
OrdinaryDiffEq.jl - High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
StochasticDiffEq.jl - Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems