C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design

• URL: http://arxiv.org/abs/2407.03333v1
• Date: Fri, 10 May 2024 01:10:49 GMT
• Title: C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design
• Authors: Noah J. Bagazinski, Faez Ahmed,
• Abstract summary: 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. This paper proposes a conditional diffusion model that generates hull designs given specific constraints.
• Score: 3.796768352477804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
  We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
  arXiv  Detail & Related papers  (2024-03-20T00:41:12Z)
• Generative VS non-Generative Models in Engineering Shape Optimization [0.3749861135832073]
  We compare the effectiveness and efficiency of generative and non-generative models in constructing design spaces. Non-generative models generate robust latent spaces with none or significantly fewer invalid designs when compared to generative models.
  arXiv  Detail & Related papers  (2024-02-13T15:45:20Z)
• Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints [53.66698106829144]
  We propose a unified model to handle a broad range of layout generation tasks. The model is based on continuous diffusion models. Experiment results show that LACE produces high-quality layouts.
  arXiv  Detail & Related papers  (2024-02-07T11:12:41Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints [4.485378844492069]
  This paper presents a study on the generation of parametric ship hull designs using a parametric diffusion model. It details adding guidance to improve the quality of generated ship hull designs. generative artificial intelligence has been shown to reduce design cycle time and create novel, high-performing designs.
  arXiv  Detail & Related papers  (2023-11-09T22:26:03Z)
• Complexity Matters: Rethinking the Latent Space for Generative Modeling [65.64763873078114]
  In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity.
  arXiv  Detail & Related papers  (2023-07-17T07:12:29Z)
• Protein Design with Guided Discrete Diffusion [67.06148688398677]
  A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. We propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods.
  arXiv  Detail & Related papers  (2023-05-31T16:31:24Z)
• Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning [4.091593765662773]
  This paper presents a large dataset of thirty thousand ship hulls, each with design and functional performance information. The paper introduces a set of twelve ship hulls from publicly available CAD repositories to showcase the proposed parameterizations ability to accurately reconstruct existing hulls.
  arXiv  Detail & Related papers  (2023-05-14T23:47:20Z)
• ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional generative model [0.0]
  We introduce ShipHullGAN, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs) At a high level, the new model intends to address the current conservatism in the parametric ship design paradigm. We trained ShipHullGAN on a large dataset of 52,591 textitphysically validated designs from a wide range of existing ship types.
  arXiv  Detail & Related papers  (2023-04-29T09:31:20Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis [10.50166876879424]
  We propose a conditional deep generative model, Range-GAN, to achieve automatic design subject to range constraints. We show that the label-aware self-augmentation leads to an average improvement of 14% on the constraint satisfaction for generated 3D shapes. We also propose a new uniformity loss to ensure generated designs evenly cover the given requirement range.
  arXiv  Detail & Related papers  (2021-03-10T18:02:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.