DamFormer: Generalizing Morphologies in Dam Break Simulations Using Transformer Model
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909364512620544 |
|---|---|
| author | Mul, Zhaoyang Liang, Aoming Ge, Mingming Chen, Dashuai Fan, Dixia Xu, Minyi |
| author_facet | Mul, Zhaoyang Liang, Aoming Ge, Mingming Chen, Dashuai Fan, Dixia Xu, Minyi |
| contents | The interaction of waves with structural barriers such as dams breaking plays a critical role in flood defense and tsunami disasters. In this work, we explore the dynamic changes in wave surfaces impacting various structural shapes, e.g., circle, triangle, and square, by using deep learning techniques. We introduce the DamFormer, a novel transformer-based model designed to learn and simulate these complex interactions. The model was trained and tested on simulated data representing the three structural forms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DamFormer: Generalizing Morphologies in Dam Break Simulations Using Transformer Model Mul, Zhaoyang Liang, Aoming Ge, Mingming Chen, Dashuai Fan, Dixia Xu, Minyi Fluid Dynamics Machine Learning The interaction of waves with structural barriers such as dams breaking plays a critical role in flood defense and tsunami disasters. In this work, we explore the dynamic changes in wave surfaces impacting various structural shapes, e.g., circle, triangle, and square, by using deep learning techniques. We introduce the DamFormer, a novel transformer-based model designed to learn and simulate these complex interactions. The model was trained and tested on simulated data representing the three structural forms. |
| title | DamFormer: Generalizing Morphologies in Dam Break Simulations Using Transformer Model |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2410.18998 |