# Incorporating structural information into Neural ODE as a Differential Equation System

**URL:** <https://discourse.julialang.org/t/incorporating-structural-information-into-neural-ode-as-a-differential-equation-system/113968>\
**Category:** General Usage\
**Tags:** flux, neural-network, differentialequation, diffeqflux\
**Created:** [May 7, 2024, 7:48pm UTC](https://discourse.julialang.org/t/incorporating-structural-information-into-neural-ode-as-a-differential-equation-system/113968 "2024-05-07T19:48:21Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![CurioMath](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/curiomath/32/43603_2.png) [@CurioMath](https://discourse.julialang.org/u/CurioMath)\
**Post date:** [May 7, 2024, 7:48pm UTC](https://discourse.julialang.org/t/incorporating-structural-information-into-neural-ode-as-a-differential-equation-system/113968/1 "2024-05-07T19:48:21Z")

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Can I know if we can feed the known physics information about a system, as a Differential Equation System into Neural network architecture within NeuralODE in DiffEqFlux.jl?

I have referred documentation examples, but found only those with known prior structural information added as a single equation within Neural Network architecture.

Thanks in advance!