论文标题
流体动力学中联合机器学习的前景
Prospects of federated machine learning in fluid dynamics
论文作者
论文摘要
基于物理学的模型已成为用于开发预测模型的流体动力学中的主流。近年来，由于数据科学，处理单元，基于神经网络的技术和传感器适应性的快速发展，机器学习为流体社区提供了复兴。到目前为止，在流体动力学中的许多应用中，机器学习方法主要集中在标准过程上，该过程需要将培训数据集中在指定机器或数据中心上。在这封信中，我们提出了一种联合机器学习方法，该方法使本地化客户能够协作学习一个汇总和共享的预测模型，同时将所有培训数据保留在每个边缘设备上。我们证明了这种分散学习方法的可行性和前景，以努力为重建时空领域建立深度学习的替代模型。我们的结果表明，联合机器学习可能是设计与流体动力学相关的高度准确预测分散的数字双胞胎的可行工具。
Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science, processing units, neural network based technologies, and sensor adaptations. So far in many applications in fluid dynamics, machine learning approaches have been mostly focused on a standard process that requires centralizing the training data on a designated machine or in a data center. In this letter, we present a federated machine learning approach that enables localized clients to collaboratively learn an aggregated and shared predictive model while keeping all the training data on each edge device. We demonstrate the feasibility and prospects of such decentralized learning approach with an effort to forge a deep learning surrogate model for reconstructing spatiotemporal fields. Our results indicate that federated machine learning might be a viable tool for designing highly accurate predictive decentralized digital twins relevant to fluid dynamics.
