Train Small, Model Big: Scalable Physics Simulators via Reduced Order Modeling and Domain Decomposition
| Autor: | Chung, Seung Whan, Choi, Youngsoo, Roy, Pratanu, Moore, Thomas, Roy, Thomas, Lin, Tiras Y., Nguyen, Du Y., Hahn, Christopher, Duoss, Eric B., Baker, Sarah E. |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 40 pages, 12 figures. Submitted to Computer Methods in Applied Mechanics and Engineering |
| Databáze: | arXiv |
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