pihnn.utils

Utilities for building and training PIHNNs.

Functions

ordinal_number(n)	Accessory function to get the ordinal number as a string from an int.
get_complex_input(input_value)
get_complex_function(func)
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
rotate_stresses(vars, angle)	Stresses and displacements transformation for rotated systems of coordinates.
compute_J_integral(model, tip[, radius, ...])	Compute the J-integral.
yau_wang_method(model, tip[, radius, ...])	Implementation of the method from Yau et al. [1980] to evaluate the
train(boundary, model, n_epochs[, learn_rate, ...])	Performs the training of the neural network.
RAD_sampling(boundary, model[, fine_grid])	Employ the RAD adaptive sampling from Wu et al. [2023].
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.,