  Numerous cutting-edge scientific technologies originate at the laboratory
scale, but transitioning them to practical industry applications is a
formidable challenge. Traditional pilot projects at intermediate scales are
costly and time-consuming. An alternative, the E-pilot, relies on high-fidelity
numerical simulations, but even these simulations can be computationally
prohibitive at larger scales. To overcome these limitations, we propose a
scalable, physics-constrained reduced order model (ROM) method. ROM identifies
critical physics modes from small-scale unit components, projecting governing
equations onto these modes to create a reduced model that retains essential
physics details. We also employ Discontinuous Galerkin Domain Decomposition
(DG-DD) to apply ROM to unit components and interfaces, enabling the
construction of large-scale global systems without data at such large scales.
This method is demonstrated on the Poisson and Stokes flow equations, showing
that it can solve equations about $15 - 40$ times faster with only $\sim$ $1\%$
relative error. Furthermore, ROM takes one order of magnitude less memory than
the full order model, enabling larger scale predictions at a given memory
limitation.
