  Numerous developments in the recent literature show the promising potential
of deep learning in obtaining numerical solutions to partial differential
equations (PDEs) beyond the reach of current numerical solvers. However,
data-driven neural operators all suffer from the same problem: the data needed
to train a network depends on classical numerical solvers such as finite
difference or finite element, among others. In this paper, we propose a new
approach to generating synthetic functional training data that does not require
solving a PDE numerically. The way we do this is simple: we draw a large number
$N$ of independent and identically distributed `random functions' $u_j$ from
the underlying solution space (e.g., $H_0^1(\Omega)$) in which we know the
solution lies according to classical theory. We then plug each such random
candidate solution into the equation and get a corresponding right-hand side
function $f_j$ for the equation, and consider $(f_j, u_j)_{j=1}^N$ as
supervised training data for learning the underlying inverse problem $f
\rightarrow u$. This `backwards' approach to generating training data only
requires derivative computations, in contrast to standard `forward' approaches,
which require a numerical PDE solver, enabling us to generate a large number of
such data points quickly and efficiently. While the idea is simple, we hope
that this method will expand the potential for developing neural PDE solvers
that do not depend on classical numerical solvers.
