A Parallel Implementation of Reduced-Order Modeling of Large-Scale Systems

2025-01-03

AIAA SCITECH 2025 Forum

AI Summary

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.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Motivated by the large-scale nature of modern aerospace engineering simulations, this paper presents a detailed description of distributed Operator Inference (dOpInf), a recently developed parallel algorithm designed to efficiently construct physics-based reduced-order models (ROMs) for problems with large state dimensions. One such example is the simulation of rotating detonation rocket engines, where snapshot data generated by high-fidelity large-eddy simulations have many millions of degrees of freedom. dOpInf enables, via distributed computing, the efficient processing of datasets with state dimensions that are too large to process on a single computer, and the learning of structured physics-based ROMs that approximate the dynamical systems underlying those datasets. All elements of dOpInf are scalable, leading to a fully parallelized reduced modeling approach that can scale to the thousands of processors available on leadership high-performance computing platforms. The resulting ROMs are computationally cheap, making them ideal for key engineering tasks such as design space exploration, risk assessment, and uncertainty quantification. To illustrate the practical application of dOpInf, we provide a step-by-step tutorial using a 2D Navier-Stokes flow over a step scenario as a case study. This tutorial guides users through the implementation process, making dOpInf accessible for integration into complex aerospace engineering simulations.

Problem

• Efficiently construct physics-based ROMs for large-scale systems
• Process massive datasets via distributed computing
• Enable scalable reduced-order modeling for aerospace simulations

Innovation

• Parallel algorithm for large-scale system modeling
• Distributed computing for high-dimensional data processing
• Scalable physics-based reduced-order models learning