Hi @Ahmed_Atallah ,
Thanks for your interest in UQLab!
It depends on your stochastic ODE. Is it stochastic because one or several parameters are stochastic? In that case: The PCE module of UQLab uses the black-box approach, i.e., it is non-intrusive. Therefore, you can apply PCE in the same way that you would apply it to any other model: You specify an experimental design (in the random parameter space), compute model evaluations, and based on this data set you fit a PCE.
Is your quantity of interest a scalar or the whole time series? In the first case, it should be quite straightforward to solve. In the second case, maybe these discussions help you:
- PCE application for a time-dependent model
- How to use sobol analysis in a 2d model with time-dependent response?
- Setting the value of certain parameters (see also Nagel, Rieckermann, Sudret (2020) )
In case your ODE is stochastic because there is some stochastic process driving the evolution, your model might actually be a stochastic simulator. Then it is not straightforward to use PCE.
Hope this helps! 
Feel free to give us more information about your model, inputs and outputs, and keep us updated about how you solve your problem.