Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model

• URL: http://arxiv.org/abs/2402.02304v5
• Date: Tue, 05 Nov 2024 21:27:42 GMT
• Title: Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model
• Authors: Luis Kaiser, Richard Tsai, Christian Klingenberg,
• Abstract summary: We present a novel unified system that integrates a numerical solver with a deep learning component into an end-to-end framework. A stable and fast solver further allows the use of Parareal, a parallel-in-time algorithm to correct high-frequency wave components.
• Score: 0.0
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• Abstract: In a variety of scientific and engineering domains, the need for high-fidelity and efficient solutions for high-frequency wave propagation holds great significance. 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.

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