DiffEqFlux
DiffEqFlux.jl - A Julia Library for Neural Differential Equations. DiffEqFlux.jl is a library for fusing neural networks and differential equations. In this work we describe differential equations from the viewpoint of data science and discuss the complementary nature between machine learning models and differential equations. We demonstrate the ability to incorporate DifferentialEquations.jl-defined differential equation problems into a Flux-defined neural network, and vice versa. The advantages of being able to use the entire DifferentialEquations.jl suite for this purpose is demonstrated by counter examples where simple integration strategies fail, but the sophisticated integration strategies provided by the DifferentialEquations.jl library succeed. This is followed by a demonstration of delay differential equations and stochastic differential equations inside of neural networks. We show high-level functionality for defining neural ordinary differential equations (neural networks embedded into the differential equation) and describe the extra models in the Flux model zoo which includes neural stochastic differential equations. We conclude by discussing the various adjoint methods used for backpropogation of the differential equation solvers. DiffEqFlux.jl is an important contribution to the area, as it allows the full weight of the differential equation solvers developed from decades of research in the scientific computing field to be readily applied to the challenges posed by machine learning and data science.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
Sorted by year (- Justin Angevaare, Zeny Feng, Rob Deardon: Infectious Disease Transmission Network Modelling with Julia (2020) arXiv
- Michael Lindner, Lucas Lincoln, Fenja Drauschke, Julia Monika Koulen, Hans Würfel, Anton Plietzsch, Frank Hellmann: NetworkDynamics.jl - Composing and simulating complex networks in Julia (2020) arXiv
- Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park: TorchDyn: A Neural Differential Equations Library (2020) arXiv
- Stefan Lenz, Maren Hackenberg, Harald Binder: The JuliaConnectoR: a functionally oriented interface for integrating Julia in R (2020) arXiv
- Chris Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, Vaibhav Dixit: DiffEqFlux.jl - A Julia Library for Neural Differential Equations (2019) arXiv