# Fitting a dynamic system with an exogenous input (nonhomogenous neural ode) via DiffEqFlux

**URL:** https://discourse.julialang.org/t/fitting-a-dynamic-system-with-an-exogenous-input-nonhomogenous-neural-ode-via-diffeqflux/44934
**Category:** New to Julia
**Tags:** package, diffeq
**Created:** [August 14, 2020, 2:19pm UTC](https://discourse.julialang.org/t/fitting-a-dynamic-system-with-an-exogenous-input-nonhomogenous-neural-ode-via-diffeqflux/44934 "2020-08-14T14:19:25Z")
**Posts on this page:** 1
**Showing post:** 25

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### Author: ![Volker](https://avatars.discourse-cdn.com/v4/letter/v/77aa72/32.png) [@Volker](https://discourse.julialang.org/u/Volker)
#### Post date: [November 6, 2020, 8:10am UTC](https://discourse.julialang.org/t/fitting-a-dynamic-system-with-an-exogenous-input-nonhomogenous-neural-ode-via-diffeqflux/44934/25 "2020-11-06T08:10:59Z")

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Thank you very much 😃

Sure you can use it for the documentation.

> [@ChrisRackauckas](#):
>
> so you were broadcasting a row vector against a column vector in the loss function to generate a matrix.

Oh no, I haven´t seen this. So simple… This was probably the main reason, why it didn´t converge

> [@ChrisRackauckas](#):
>
> If you don’t have very much data but you have a model then this could be a way to get a better predicting method since the neural network will likely overfit and attempt to over-generalize. There’s also a ton of other reasons, like how discretized physics-informed neural networks are a subset of UDEs and how high dimensional PDE solvers can be written as a UDE. See [https://arxiv.org/abs/2001.04385](https://arxiv.org/abs/2001.04385) for all of the details.

Thanks, I will read it carefully.

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