C-ShipGen：一种用于参数化船体设计的条件引导扩散模型
C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design
AI总结：
本文提出条件引导扩散模型C-ShipGen，在给定主尺度等约束下生成低阻力船体设计，无需重新训练，五个测试用例中总阻力较优化船体降低超过25%，可显著缩短船舶设计周期。
摘要：
船舶设计是一个复杂的设计过程，一支海军建筑师团队可能需要多年才能完成。改进船舶设计过程可以显著节省成本，同时仍能为客户提供高质量的设计。船体设计的一项新技术是扩散模型，这是一种生成式人工智能。先前使用扩散模型进行船体设计的工作生成了高质量船体，具有更低的阻力和更大的排水体积。然而，该工作无法生成满足特定设计约束的船体。本文提出了一种条件扩散模型，在给定特定约束（例如船体期望的主尺度）的情况下生成船体设计。此外，该扩散模型利用来自总阻力回归模型的梯度来创建低阻力设计。五个设计测试用例将该扩散模型与设计优化算法进行了比较，以创建低阻力船体设计。在所有五个测试用例中，该扩散模型均生成了多样化的设计，其总阻力低于优化后的船体，阻力降低超过25%。该扩散模型还无需重新训练即可生成这些设计。这项工作可以通过数据驱动的方法创建满足用户要求的高质量船体，从而显著缩短船舶的设计周期。
英文摘要：
Ship design is a complex design process that may take a team of naval architects many years to complete. Improving the ship design process can lead to significant cost savings, while still delivering high-quality designs to customers. A new technology for ship hull design is diffusion models, a type of generative artificial intelligence. Prior work with diffusion models for ship hull design created high-quality ship hulls with reduced drag and larger displaced volumes. However, the work could not generate hulls that meet specific design constraints. This paper proposes a conditional diffusion model that generates hull designs given specific constraints, such as the desired principal dimensions of the hull. In addition, this diffusion model leverages the gradients from a total resistance regression model to create low-resistance designs. Five design test cases compared the diffusion model to a design optimization algorithm to create hull designs with low resistance. In all five test cases, the diffusion model was shown to create diverse designs with a total resistance less than the optimized hull, having resistance reductions over 25%. The diffusion model also generated these designs without retraining. This work can significantly reduce the design cycle time of ships by creating high-quality hulls that meet user requirements with a data-driven approach.
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。
