Physics-Informed Neural Network (PINN) Implementation for PDE Solution

The code snippet initializes the class for solving a PDE using a physics-informed neural network (PINN) approach.

The constructor takes in several parameters including the training data (X_u, E, Ne, Te, X_f), the neural network architecture (layers), and lower and upper bounds for the input variables (lb, ub).

The constructor sets the lower and upper bounds, extracts the input and output variables from the training data, initializes the neural network weights and biases, and creates TensorFlow placeholders for the input data.

It also defines the neural network model, loss function, and optimization algorithm. The loss function consists of three terms: the mean squared error between the predicted and actual values of E, Ne, and Te, respectively.

The constructor also creates a TensorFlow session and initializes the variables.