In practical engineering, the layout optimization technique driven by the thermal performance is faced with a severe computational burden when directly integrating the numerical analysis tool of temperature simulation into the optimization loop. To alleviate this difficulty, this paper presents a novel deep learning surrogate-assisted heat source layout optimization method. First, two sampling strategies, namely the random sampling strategy and the evolving sampling strategy, are proposed to produce diversified training data. Then, regarding mapping between the layout and the corresponding temperature field as an image-to-image regression task, the feature pyramid network (FPN), a kind of deep neural network, is trained to learn the inherent laws, which plays as a surrogate model to evaluate the thermal performance of the domain with respect to different input layouts accurately and efficiently. Finally, the neighborhood search-based layout optimization (NSLO) algorithm is proposed and combined with the FPN surrogate to solve discrete heat source layout optimization problems. A typical two-dimensional heat conduction optimization problem is investigated to demonstrate the feasibility and effectiveness of the proposed deep learning surrogate-assisted layout optimization framework.

在实际工程中，将温度模拟的数值分析工具直接集成到优化循环中时，由热性能驱动的布局优化技术面临着巨大的计算负担。为了缓解这一困难，本文提出了一种新颖的深度学习替代辅助热源布局优化方法。首先，提出了两种抽样策略，即随机抽样策略和演进抽样策略，以产生多样化的训练数据。然后，将布局和相应温度场之间的映射作为图像到图像的回归任务，对特征金字塔网络（FPN）（一种深度神经网络）进行了训练，以学习其固有规律，用作替代模型，以相对于不同的输入布局准确有效地评估域的热性能。最后，提出了基于邻域搜索的布局优化算法，并结合FPN替代算法解决了离散热源布局优化问题。研究了一个典型的二维热传导优化问题，以证明所提出的深度学习替代辅助布局优化框架的可行性和有效性。