How to predict new value corresponding to a new initial condition when using NeuralPDE

After using NeuralPDE to solve the ODEs system, I got the prediction for a specific system as a result of the training process by using the code in the tutorial below:

ts = 0.0:0.01:10.0
minimizers_ = [res.u.depvar[sym_prob.depvars[i]] for i in 1:2]
u_predict  = [[phi[i]([t],minimizers_[i])[1] for t in ts] for i in 1:2]

Please help me with how could I change the input (when changing the initial conditions) and use the training weights to predict a new value.

Hi @Ethan_Tran, run your code with the new problem which uses the new initial conditions when specifying the ODE system, and use the new results(trained parameters) for predicting over your testing domain. I could help more if you tell me what problem are you working with, and what new initial conditions are you exactly talking about.