Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2411.12948 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909695270191104 |
|---|---|
| author | McDugald, Edward Mohan, Arvind Engwirda, Darren Marcato, Agnese Santos, Javier |
| author_facet | McDugald, Edward Mohan, Arvind Engwirda, Darren Marcato, Agnese Santos, Javier |
| contents | We investigate the potential of an attention-based neural network architecture, the Senseiver, for sparse sensing in tsunami forecasting. Specifically, we focus on the Tsunami Data Assimilation Method, which generates forecasts from tsunameter networks. Our model is used to reconstruct high-resolution tsunami wavefields from extremely sparse observations, including cases where the tsunami epicenters are not represented in the training set. Furthermore, we demonstrate that our approach significantly outperforms the Linear Interpolation with Huygens-Fresnel Principle in generating dense observation networks, achieving markedly improved accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12948 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Attention-Based Reconstruction of Full-Field Tsunami Waves from Sparse Tsunameter Networks McDugald, Edward Mohan, Arvind Engwirda, Darren Marcato, Agnese Santos, Javier Machine Learning Fluid Dynamics We investigate the potential of an attention-based neural network architecture, the Senseiver, for sparse sensing in tsunami forecasting. Specifically, we focus on the Tsunami Data Assimilation Method, which generates forecasts from tsunameter networks. Our model is used to reconstruct high-resolution tsunami wavefields from extremely sparse observations, including cases where the tsunami epicenters are not represented in the training set. Furthermore, we demonstrate that our approach significantly outperforms the Linear Interpolation with Huygens-Fresnel Principle in generating dense observation networks, achieving markedly improved accuracy. |
| title | Attention-Based Reconstruction of Full-Field Tsunami Waves from Sparse Tsunameter Networks |
| topic | Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/2411.12948 |