Machine learning techniques have inspired reduced-order solutions in the fluid mechanics field that show benefits of unprecedented capability and efficiency. Targeting ocean-wave problems, this work has developed a novel data-driven computational approach, named Wave-GAN. This new tool is based upon the conditional Generative Adversarial Network (GAN) principle, and it provides the ability to predict three-dimensional nonlinear wave loads and run-up on a fixed structure. The paper presents the principle of Wave-GAN and an application example of regular waves interacting with a vertical fixed cylinder. Computational Fluid Dynamics (CFD) is used to provide training and testing datasets for the Wave-GAN deep learning network. Upon verification, Wave-GAN proved the ability to provide accurate results for predicting wave load and run-up for wave conditions that were not informed during training. Yet the CFD-comparative results were only obtained within seconds by the deep learning tool. The promising results demonstrate Wave-GAN's outstanding potential to act as a pioneering sample of applying machine learning techniques to wave-structural interaction problems. It is envisioned that the new approach could be extended to more complex shapes and wave conditions to facilitate the various design stages of marine and offshore engineering applications such as monopiles. As a result, enhanced reliability is expected to optimise structural performance and prevent environmental disasters.

机器学习技术启发了流体力学领域的降阶解决方案，这些解决方案显示出前所未有的能力和效率。针对海浪问题，这项工作开发了一种新颖的数据驱动计算方法，称为Wave-GAN。此新工具基于条件生成对抗网络（GAN）原理，并且提供了预测三维非线性波载荷和在固定结构上加速的功能。本文介绍了Wave-GAN的原理以及规则波与垂直固定圆柱相互作用的应用示例。计算流体动力学（CFD）用于为Wave-GAN深度学习网络提供训练和测试数据集。验证后，Wave-GAN证明了为训练期间未告知的波浪状况提供准确结果以预测波浪荷载和加速的能力。然而，深度学习工具仅在几秒钟内获得了CFD对比结果。令人鼓舞的结果表明Wave-GAN具有出色的潜力，可以作为将机器学习技术应用于波浪结构相互作用问题的先驱样本。可以预见，可以将新方法扩展到更复杂的形状和波浪条件，以促进海洋工程和近海工程应用（例如单桩）的各个设计阶段。结果，增强的可靠性有望优化结构性能并防止环境灾难。然而，深度学习工具仅在几秒钟内获得了CFD对比结果。令人鼓舞的结果表明Wave-GAN具有出色的潜力，可以作为将机器学习技术应用于波浪结构相互作用问题的先驱样本。可以预见，可以将新方法扩展到更复杂的形状和波浪条件，以促进海洋工程和近海工程应用（例如单桩）的各个设计阶段。结果，增强的可靠性有望优化结构性能并防止环境灾难。然而，深度学习工具仅在几秒钟内获得了CFD对比结果。令人鼓舞的结果表明Wave-GAN具有出色的潜力，可以作为将机器学习技术应用于波浪结构相互作用问题的先驱样本。可以预见，可以将新方法扩展到更复杂的形状和波浪条件，以促进海洋工程和近海工程应用（例如单桩）的各个设计阶段。结果，增强的可靠性有望优化结构性能并防止环境灾难。可以预见，可以将新方法扩展到更复杂的形状和波浪条件，以促进海洋工程和近海工程应用（例如单桩）的各个设计阶段。结果，增强的可靠性有望优化结构性能并防止环境灾难。可以预见，可以将新方法扩展到更复杂的形状和波浪条件，以促进海洋工程和近海工程应用（例如单桩）的各个设计阶段。结果，增强的可靠性有望优化结构性能并防止环境灾难。