A Parallel Implementation of Reduced-Order Modeling of Large-Scale Systems
For large-scale aerospace simulations—such as rotating detonation rocket engines—with state dimensions reaching tens of millions, conventional reduced-order modeling (ROM) becomes infeasible on a single machine. This work proposes distributed Operator Inference (dOpInf), the first framework enabling fully scalable, physics-constrained ROM construction. dOpInf integrates hybrid MPI/OpenMP parallelism, distributed linear algebra, proper orthogonal decomposition (POD) projection, and structured system identification. Deployed on high-performance computing platforms, it overcomes memory and computational bottlenecks inherent to monolithic ROM training, enabling highly concurrent ROM construction across thousands of CPU cores. Validated on a 2D channel flow problem, the resulting ROM preserves physical consistency while achieving extreme model compactness and a 100× speedup over full-order simulation. This efficiency facilitates computationally intensive engineering tasks, including design space exploration and uncertainty quantification.