Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine
<|reference_start|>Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine: High-performance computing (HPC) has revolutionized our ability to perform detailed simulations of complex real-world processes. A prominent contemporary example is from aerospace propulsion, where HPC is used for rotating detonation rocket engine (RDRE) simulations in support of the design of next-generation rocket engines; however, these simulations take millions of core hours even on powerful supercomputers, which makes them impractical for engineering tasks like design exploration and risk assessment. Reduced-order models (ROMs) address this limitation by constructing computationally cheap yet sufficiently accurate approximations that serve as surrogates for the high-fidelity model. This paper contributes a new distributed algorithm that achieves fast and scalable construction of predictive physics-based ROMs trained from sparse datasets of extremely large state dimension. The algorithm learns structured physics-based ROMs that approximate the dynamical systems underlying those datasets. This enables model reduction for problems at a scale and complexity that exceeds the capabilities of existing approaches. We demonstrate our algorithm's scalability using up to $2,048$ cores on the Frontera supercomputer at the Texas Advanced Computing Center. We focus on a real-world three-dimensional RDRE for which one millisecond of simulated physical time requires one million core hours on a supercomputer. Using a training dataset of $2,536$ snapshots each of state dimension $76$ million, our distributed algorithm enables the construction of a predictive data-driven reduced model in just $13$ seconds on $2,048$ cores on Frontera.<|reference_end|>
@article{farcas2024distributed,
    title={Distributed computing for physics-based data-driven reduced modeling at
  scale: Application to a rotating detonation rocket engine},
    author={Ionut-Gabriel Farcas, Rayomand P. Gundevia, Ramakanth Munipalli, Karen
  E. Willcox},
    journal={arXiv preprint arXiv:2407.09994},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09994},
    primaryClass={math.NA cs.DC cs.LG cs.NA}
}