Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model
<|reference_start|>Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model: Recent advances in wave modeling use sufficiently accurate fine solver outputs to train a neural network that enhances the accuracy of a fast but inaccurate coarse solver. In this paper we build upon the work of Nguyen and Tsai (2023) and present a novel unified system that integrates a numerical solver with a deep learning component into an end-to-end framework. In the proposed setting, we investigate refinements to the network architecture and data generation algorithm. A stable and fast solver further allows the use of Parareal, a parallel-in-time algorithm to correct high-frequency wave components. Our results show that the cohesive structure improves performance without sacrificing speed, and demonstrate the importance of temporal dynamics, as well as Parareal, for accurate wave propagation.<|reference_end|>
@article{kaiser2024efficient,
    title={Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep
  Learning Model},
    author={Luis Kaiser, Richard Tsai, Christian Klingenberg},
    journal={arXiv preprint arXiv:2402.02304},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02304},
    primaryClass={math.AP cs.LG}
}