Accelerating wave simulations with neural dispersion correctors
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918156458524672 |
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| author | Rincón, Felipe Fichtner, Andreas Aleardi, Mattia Tognarelli, Andrea Stucchi, Eusebio |
| author_facet | Rincón, Felipe Fichtner, Andreas Aleardi, Mattia Tognarelli, Andrea Stucchi, Eusebio |
| contents | We present a Fourier neural operator network, designed to correct dispersion errors in numerical wave simulations. The neural dispersion corrector enables the replacement of a computationally expensive high-accuracy simulation by a less expensive low-accuracy simulation. In contrast to neural network surrogates that fully replace a wave equation, the neural dispersion corrector has only a weak dependence on the distribution of model parameters, such as wave speeds. Consequently, the network can be trained with a significantly smaller dataset, while still generalising to unseen input parameters. Following a description of the network architecture and training, we provide examples for the 3-D elastic wave equation. After training with merely 1$\,$000 examples on one GPU, the neural corrector achieves a speed-up of 16$\times$ compared to a reference spectral-element simulation and a generalisation to a broad range of strongly heterogeneous wave speed distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06881 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating wave simulations with neural dispersion correctors Rincón, Felipe Fichtner, Andreas Aleardi, Mattia Tognarelli, Andrea Stucchi, Eusebio Geophysics 65M99, 86A15, 35Q68 I.6.8; G.1.8; J.2 We present a Fourier neural operator network, designed to correct dispersion errors in numerical wave simulations. The neural dispersion corrector enables the replacement of a computationally expensive high-accuracy simulation by a less expensive low-accuracy simulation. In contrast to neural network surrogates that fully replace a wave equation, the neural dispersion corrector has only a weak dependence on the distribution of model parameters, such as wave speeds. Consequently, the network can be trained with a significantly smaller dataset, while still generalising to unseen input parameters. Following a description of the network architecture and training, we provide examples for the 3-D elastic wave equation. After training with merely 1$\,$000 examples on one GPU, the neural corrector achieves a speed-up of 16$\times$ compared to a reference spectral-element simulation and a generalisation to a broad range of strongly heterogeneous wave speed distributions. |
| title | Accelerating wave simulations with neural dispersion correctors |
| topic | Geophysics 65M99, 86A15, 35Q68 I.6.8; G.1.8; J.2 |
| url | https://arxiv.org/abs/2510.06881 |