2023/12/06 by Seung Whan Chung, Chung, Seung Whan, Youngsoo Choi +17 · 5 citations
Engineering · Physics and Astronomy · #65F55 #65N55 (primary) 76D07 (secondary) #Computational Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Vibration Analysis #Model Reduction and Neural Networks #Real-time simulation and control systems #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2401.10245
openalex publication_date 2023/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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 ∼ 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.