Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep
Learning Model
- URL: http://arxiv.org/abs/2402.02304v4
- Date: Tue, 13 Feb 2024 20:14:28 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
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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