标题： 从受限的空气动力学数据集进行快速神经网络预测 显示英文标题

标题： Fast Neural Network Predictions from Constrained Aerodynamics Datasets

摘要： 在喷气式飞机、航天器或燃气轮机发动机的设计过程中，引入计算流体力学通常受到所需的计算资源和仿真时间的挑战，而这取决于所选择的基于物理的计算模型和网格分辨率。 该领域的一个持续问题是，如何更快地模拟这些系统，同时仍保持足够的准确性。 虽然许多方法涉及简化底层物理模型，但其他方法则是无模型的，并且仅基于现有的仿真数据进行预测。 我们提出了一种新的无模型方法，通过重新构建仿真问题来有效地增加约束预计算数据集的规模，并引入一种新颖的神经网络架构（称为聚类网络），该架构具有适合高度非线性计算流体力学解的归纳偏差。 与基于模型的近似方法相比，我们证明我们的方法几乎同样准确，速度快一个数量级，并且更容易应用。 此外，我们展示了我们的方法优于其他无模型方法。 显示英文摘要

摘要： Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simulate these systems faster but with sufficient accuracy. While many approaches involve simplified models of the underlying physics, others are model-free and make predictions based only on existing simulation data. We present a novel model-free approach in which we reformulate the simulation problem to effectively increase the size of constrained pre-computed datasets and introduce a novel neural network architecture (called a cluster network) with an inductive bias well-suited to highly nonlinear computational fluid dynamics solutions. Compared to the state-of-the-art in model-based approximations, we show that our approach is nearly as accurate, an order of magnitude faster, and easier to apply. Furthermore, we show that our method outperforms other model-free approaches.