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Numerous cutting-edge scientific technologies originate at the laboratoryscale, but transitioning them to practical industry applications is aformidable challenge. Traditional pilot projects at intermediate scales arecostly and time-consuming. An alternative, the E-pilot, relies on high-fidelitynumerical simulations, but even these simulations can be computationallyprohibitive at larger scales. To overcome these limitations, we propose ascalable, physics-constrained reduced order model (ROM) method. ROM identifiescritical physics modes from small-scale unit components, projecting governingequations onto these modes to create a reduced model that retains essentialphysics details. We also employ Discontinuous Galerkin Domain Decomposition(DG-DD) to apply ROM to unit components and interfaces, enabling theconstruction of large-scale global systems without data at such large scales.This method is demonstrated on the Poisson and Stokes flow equations, showingthat it can solve equations about $15 - 40$ times faster with only $\sim$ $1\%$relative error. Furthermore, ROM takes one order of magnitude less memory thanthe full order model, enabling larger scale predictions at a given memorylimitation.

Train Small, Model Big: Scalable Physics Simulators via Reduced Order Modeling and Domain Decomposition