As I see, you are looking for an explanation of the function “cat(1, X, T)” which concatenates on dimension 1 for the following Physics-Informed Neural Network (PINN) example:
openExample('nnet/TrainPhysicsInformedNeuralNetworkWithLBFGSAndAutoDiffExample’)
In this example, the network expects input features of size “2C x 1B”, meaning 2 features with a batch size of “B”. You can visualize the network using:
analyzeNetwork(net)
In the example, the dimensions of “X”, “T”, “X0”, and “T0” are in the data format “1(C) x 10000(B) and 1(C) x 100(B)” respectively. Using “cat(1, X, T)”, you create an input feature of size “2(C) x 10000(B)”, which matches the feature input size expected by the neural network. On the other hand, “cat(2, X, T)” would create a size of “1(C) x 20000(B)” on dimension two.
For more information on data format and functions on “dlarray” refer to the following link:
