Reduced-order modeling of fluid flows with transformers
Reduced-order modeling of fluid flows with transformers
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DOI：
10.1063/5.0151515
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发表时间：
2023-05
影响因子：
4.6
通讯作者：
AmirPouya Hemmasian;Amir Barati Farimani
中科院分区：
文献类型：
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作者：
AmirPouya Hemmasian;Amir Barati Farimani
流体流动的降阶建模（ROM）几十年来一直是一个活跃的研究领域。直接数值模拟的巨大计算成本促使研究人员开发更有效的替代方法，例如 ROM 和其他替代模型。与计算机视觉和语言建模等许多应用领域类似，机器学习和数据驱动方法在流体动力学新型模型的开发中发挥了重要作用。 Transformer是最先进的深度学习架构之一，近年来在人工智能的许多应用领域取得了多项突破，包括但不限于自然语言处理、图像处理和视频处理。在这项工作中，我们研究了该架构在 ROM 框架中学习流体流动动力学的能力。我们使用卷积自动编码器作为降维机制，并训练变压器模型来学习系统在编码状态空间中的动态。即使对于湍流数据集，该模型也显示出有竞争力的结果。
Reduced-order modeling (ROM) of fluid flows has been an active area of research for several decades. The huge computational cost of direct numerical simulations has motivated researchers to develop more efficient alternative methods, such as ROMs and other surrogate models. Similar to many application areas, such as computer vision and language modeling, machine learning and data-driven methods have played an important role in the development of novel models for fluid dynamics. The transformer is one of the state-of-the-art deep learning architectures that has made several breakthroughs in many application areas of artificial intelligence in recent years, including but not limited to natural language processing, image processing, and video processing. In this work, we investigate the capability of this architecture in learning the dynamics of fluid flows in a ROM framework. We use a convolutional autoencoder as a dimensionality reduction mechanism and train a transformer model to learn the system's dynamics in the encoded state space. The model shows competitive results even for turbulent datasets.