Hi everyone,

I have a very basic question about solving optimal control problems with universal differential equations. Say I want to find a control `c(t)`

to achieve a certain final state `u(T)`

, and I can use neural networks to learn the differential equation model so that the optimal contral `c(t)`

can be naturally solved. If I change the desired final state `u(T)`

, should I re-train the neural network to learn the model again?

Personally I think I should train the neural network again since the model has been changed, based on the tutorials provided in the docs of related julia packages. However, is there a way to learn the â€śinverse transformâ€ť of the optimal control problem, so that the neural work does not need be trained again?

Thanks!

Xiaodong

The underlying dynamical system, i.e. `x' = f(x,u,t)`

should still be the same, so it should be fine. However, a different choice of `u`

could perturb it to a place in phase space that it didnâ€™t train on before. To handle this, you can use the current coefficients of the neural network and perform whatâ€™s known as transfer learning, i.e. use the trained network of the old model as the initial conditions for the network of the new model.

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Thank you Chris! I will have a try. The suggestion to use transfer learning is really great.

Classic example of machine learning people coming up with new names for ancient concepts

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