# Examples of dynamical physical systems with exogenous inputs described by UDEs

**URL:** <https://discourse.julialang.org/t/examples-of-dynamical-physical-systems-with-exogenous-inputs-described-by-udes/133373>\
**Category:** Modelling & Simulations\
**Tags:** diffeq, sciml\
**Created:** [October 23, 2025, 8:20am UTC](https://discourse.julialang.org/t/examples-of-dynamical-physical-systems-with-exogenous-inputs-described-by-udes/133373 "2025-10-23T08:20:08Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![Emil\_Martinsen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/emil_martinsen/32/219121_2.png) [@Emil\_Martinsen](https://discourse.julialang.org/u/Emil_Martinsen)\
**Post date:** [October 27, 2025, 3:31pm UTC](https://discourse.julialang.org/t/examples-of-dynamical-physical-systems-with-exogenous-inputs-described-by-udes/133373/3 "2025-10-27T15:31:56Z")

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Hi Chris, thx for your reply. Fair. Yes I use DataInterpolations.jl for the inputs. My main challenge is that the gradient computation of the loss functions seems to be extremely slow with my setup. It seems like something is wrong or done in a inefficient way.

So I made a new post with a MWE of my setup - maybe you or someone else can see what is missing.

Link to post:

> [@AD of UDEs - how to set it up?](https://discourse.julialang.org/t/ad-of-udes-how-to-set-it-up/133461):
>
> Hi! Im new to Julia and SciML but I am really curious to see how combining ODE’s with neural network could improve the predictive performance of the model we are developing for solvent-based CO2 capture. My current setup includes dynamic states, multiple independent timeseries of real data exogenous inputs, unknown dynamics modelled as a neural network (NN). I use DataInterpolations.jl to handle the inputs. I want to train the NN by minimising a loss function (MSE across all the independent…

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