基于DNN的主动学习：基于自模拟数据集的流体动力学自动工程设计优化
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset
摘要：
优化流体动力学性能是一项重要的工程任务。传统上，专家根据经验估计设计形状，并通过昂贵的实验来验证它们。这个在时间和空间方面都昂贵的过程可能只探索有限数量的形状，并导致次优设计。在这项研究中，一个测试证明的深度学习架构被应用于预测各种限制下的性能，并通过优化学习的预测函数来搜索更好的形状。主要的挑战是深度神经网络（DNN）需要大量的数据点，这是无法模拟的。为了弥补这个缺点，使用频率主义主动学习来探索DNN预测有希望的输出空间的区域。此操作将所需的数据样本数量从约8000减少到625。最后一个阶段，一个用户界面，使模型能够优化与给定的用户输入的最小面积和粘度。整体填充用于定义边界面积函数，以便最佳形状不会绕过最小面积。随机梯度朗之万动力学（SGLD），以确保最终的形状是优化的，同时绕过所需的面积。共同地，具有极低阻力的形状被发现由实用的用户界面探索，没有人类领域知识和适度的计算开销。
英文摘要：
Optimizing fluid-dynamic performance is an important engineering task. Traditionally, experts design shapes based on empirical estimations and verify them through expensive experiments. This costly process, both in terms of time and space, may only explore a limited number of shapes and lead to sub-optimal designs. In this research, a test-proven deep learning architecture is applied to predict the performance under various restrictions and search for better shapes by optimizing the learned prediction function. The major challenge is the vast amount of data points Deep Neural Network (DNN) demands, which is improvident to simulate. To remedy this drawback, a Frequentist active learning is used to explore regions of the output space that DNN predicts promising. This operation reduces the number of data samples demanded from ~8000 to 625. The final stage, a user interface, made the model capable of optimizing with given user input of minimum area and viscosity. Flood fill is used to define a boundary area function so that the optimal shape does not bypass the minimum area. Stochastic Gradient Langevin Dynamics (SGLD) is employed to make sure the ultimate shape is optimized while circumventing the required area. Jointly, shapes with extremely low drags are found explored by a practical user interface with no human domain knowledge and modest computation overhead.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
