用深度学习从压力分布反推最优船尾形状，省去传统试错迭代。
Inverse Design of Optimal Stern Shape with Convolutional Neural Network-based Pressure Distribution
- 用卷积神经网络提取压力分布轮廓特征，多任务学习预测船尾截面控制点。
- 通过B样条控制点重构船尾形状，实测阻力与推进效率达标。
- 适合船舶设计、流体力学优化领域，尤其关注高效逆向设计的团队。
船体线型设计是一个迭代过程，需通过计算流体动力学或模型试验评估性能。船尾形状需根据压力分布分析结果反复修改，直至满足阻力与推进效率要求。本文中，设计师先设定符合需求的压力分布，提出一种基于深度学习的逆向设计算法：利用卷积神经网络提取压力分布轮廓特征，采用多任务学习模型估计船尾各截面的形状。通过预测B样条控制点并对比实际与重构的型线偏差，间接恢复船尾形状。最终性能验证表明该方法可有效实现逆向设计。
原文摘要 · Abstract (English)
Hull form designing is an iterative process wherein the performance of the hull form needs to be checked via computational fluid dynamics calculations or model experiments. The stern shape has to undergo a process wherein the hull form variations from the pressure distribution analysis results are repeated until the resistance and propulsion efficiency meet the design requirements. In this study, the designer designed a pressure distribution that meets the design requirements; this paper proposes an inverse design algorithm that estimates the stern shape using deep learning. A convolutional neural network was used to extract the features of the pressure distribution expressed as a contour, whereas a multi-task learning model was used to estimate various sections of the stern shape. We estimated the stern shape indirectly by estimating the control point of the B-spline and comparing the actual and converted offsets for each section; the performance was verified, and an inverse design is proposed herein
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