  A method is presented that allows to reduce a problem described by
differential equations with initial and boundary conditions to the problem
described only by differential equations. The advantage of using the modified
problem for physics-informed neural networks (PINNs) methodology is that it
becomes possible to represent the loss function in the form of a single term
associated with differential equations, thus eliminating the need to tune the
scaling coefficients for the terms related to boundary and initial conditions.
The weighted loss functions respecting causality were modified and new weighted
loss functions based on generalized functions are derived. Numerical
experiments have been carried out for a number of problems, demonstrating the
accuracy of the proposed methods.
