  Electrical circuits are present in a variety of technologies, making their
design an important part of computer aided engineering. The growing number of
parameters that affect the final design leads to a need for new approaches to
quantify their impact. Machine learning may play a key role in this regard,
however current approaches often make suboptimal use of existing knowledge
about the system at hand. In terms of circuits, their description via modified
nodal analysis is well-understood. This particular formulation leads to systems
of differential-algebraic equations (DAEs) which bring with them a number of
peculiarities, e.g. hidden constraints that the solution needs to fulfill. We
use the recently introduced dissection index that can decouple a given system
of DAEs into ordinary differential equations, only depending on differential
variables, and purely algebraic equations, that describe the relations between
differential and algebraic variables. The idea is to then only learn the
differential variables and reconstruct the algebraic ones using the relations
from the decoupling. This approach guarantees that the algebraic constraints
are fulfilled up to the accuracy of the nonlinear system solver, and it may
also reduce the learning effort as only the differential variables need to be
learned.
