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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.06433 |
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| _version_ | 1866914455653187584 |
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| author | Cai, Shukai Dutta, Sourav Loveland, Mark Valseth, Eirik Rivera-Casillas, Peter Trahan, Corey Dawson, Clint |
| author_facet | Cai, Shukai Dutta, Sourav Loveland, Mark Valseth, Eirik Rivera-Casillas, Peter Trahan, Corey Dawson, Clint |
| contents | Wave setup plays a significant role in transferring wave-induced energy to currents and causing an increase in water elevation. This excess momentum flux, known as radiation stress, motivates the coupling of circulation models with wave models to improve the accuracy of storm surge prediction, however, traditional numerical wave models are complex and computationally expensive. As a result, in practical coupled simulations, wave models are often executed at much coarser temporal resolution than circulation models. In this work, we explore the use of Deep Operator Networks (DeepONets) as a surrogate for the Simulating WAves Nearshore (SWAN) numerical wave model. The proposed surrogate model was tested on three distinct 1-D and 2-D steady-state numerical examples with variable boundary wave conditions and wind fields. When applied to a realistic numerical example of steady state wave simulation in Duck, NC, the model achieved consistently high accuracy in predicting the components of the radiation stress gradient and the significant wave height across representative scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06433 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Operator Learning for Surrogate Modeling of Wave-Induced Forces from Sea Surface Waves Cai, Shukai Dutta, Sourav Loveland, Mark Valseth, Eirik Rivera-Casillas, Peter Trahan, Corey Dawson, Clint Computational Physics Machine Learning Fluid Dynamics Wave setup plays a significant role in transferring wave-induced energy to currents and causing an increase in water elevation. This excess momentum flux, known as radiation stress, motivates the coupling of circulation models with wave models to improve the accuracy of storm surge prediction, however, traditional numerical wave models are complex and computationally expensive. As a result, in practical coupled simulations, wave models are often executed at much coarser temporal resolution than circulation models. In this work, we explore the use of Deep Operator Networks (DeepONets) as a surrogate for the Simulating WAves Nearshore (SWAN) numerical wave model. The proposed surrogate model was tested on three distinct 1-D and 2-D steady-state numerical examples with variable boundary wave conditions and wind fields. When applied to a realistic numerical example of steady state wave simulation in Duck, NC, the model achieved consistently high accuracy in predicting the components of the radiation stress gradient and the significant wave height across representative scenarios. |
| title | Operator Learning for Surrogate Modeling of Wave-Induced Forces from Sea Surface Waves |
| topic | Computational Physics Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/2604.06433 |