  We present a novel approach to modelling and learning vector fields from
physical systems using neural networks that explicitly satisfy known linear
operator constraints. To achieve this, the target function is modelled as a
linear transformation of an underlying potential field, which is in turn
modelled by a neural network. This transformation is chosen such that any
prediction of the target function is guaranteed to satisfy the constraints. The
approach is demonstrated on both simulated and real data examples.
