pihnn.utils

Utilities for building and training PIHNNs.

Functions

get_complex_input(input_value)
get_complex_function(func)
derivative(f, z[, holom, conjugate])	Compute the derivative \(\frac{df}{dz}\) through PyTorch automatic differentiation.
MSE(value[, true_value])	Mean squared error (MSE). Equivalent to torch.nn.MSELoss() except it takes into account empty inputs.
PIHNNloss(boundary, model, t)	Evaluation of the loss function as Mean squared error (MSE).
scalar_loss(boundary, model, t)	Called by pihnn.utils.PIHNNloss() if one aims to solve the Laplace or biharmonic problem.
km_loss(boundary, model, t)	Called by pihnn.utils.PIHNNloss() if one aims to solve the linear elasticity problem
train(boundary, model, n_epochs[, learn_rate, ...])	Performs the training of the neural network.
compute_Lp_error(triangulation, model, model_true[, p])	Compute and print to screen the approximated relative \(L^p\) error between a model and a reference solution. I.e.,