← Back to all news
arXiv AI By Hadi Keramati, Mohammed A. Hannan, Patrick Kirchen, Sina Tousi, Joonas Riekkinen, Jim Pyra, Rajeev K. Jaiman

Localized Generative Design of Ship Hulls with Diffusion Models and High-Fidelity Assessment

Read the original on arXiv AI →

The paper introduces a reward‑directed diffusion framework for localized ship hull optimization, using parametric masking to focus changes on specific hull regions while maintaining overall continuity. Surrogate‑assisted sampling targets low calm‑water resistance, and a high‑fidelity CFD pipeline evaluates the designs, revealing trade‑offs between friction and pressure that are not captured by empirical screening. The study demonstrates that detailed assessment can uncover limitations in the generative model and guide better objective formulations for realistic hull designs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Jun 5

3D Underwater Path Planning via Generative Flow Field Surrogates

arXiv:2606. 06077v1 Announce Type: cross Abstract: Autonomous underwater vehicle (AUV) launch and recovery (LAR) into the hull of an advancing host platform requires traversal of a complex, three-dimensional propeller wake whose hydrodynamic structure cannot be characterised by a uniform current model.

By Zachary Cooper-Baldock, Paulo E. Santos, Russell S. A. Brinkworth, Karl Sammut
agentsroboticsbenchmarkssafety
More like this →
arXiv Machine Learning
Oct 12

Reinforcement learning for inverse silhouette design and rapid laser cutting of compact parallelogram quad kirigami prototypes

The paper introduces RL‑Kirigami, an inverse design framework that uses optimal‑transport conditional flow matching (OT‑CFM) and reinforcement learning to generate compatible ratio fields for compact reconfigurable parallelogram quad kirigami. A marching decoder enforces global geometric compatibility, while Group Relative Policy Optimization (GRPO) aligns the generator with nondifferentiable rewards for silhouette matching, feasibility, and ratio‑field regularity. The method achieves high silhouette IoU scores (up to 94.91%) and reduces forward simulator evaluations from hundreds to one, enabling rapid laser‑cut prototypes in polymeric sheets within about eight minutes.

By Milad Yazdani, Shahriar Shalileh, Dena Shahriari
diffusionreinforcement-learning
More like this →
arXiv Machine Learning
Sep 4

Learnable composition for neural operators

The paper introduces LatentDDM, a neural operator framework that first pretrains on small subdomains and then adapts to new settings by training only a lightweight composition module. Experiments on steady Darcy flow and unsteady airfoil flow show that this approach reduces error by 36‑56% on larger domains and improves 20‑step rollouts, outperforming capacity‑matched full‑domain models. The study highlights co‑designed local pretraining and composition‑level transfer as a promising design principle for physical foundation models.

By Zituo Chen, Baiming Zhang, Sili Deng
More like this →
arXiv Machine Learning
Sep 2

Conditional Flow Matching for ML-Based Inverse Design Problems

The paper introduces Conditional Flow Matching (CFM) for engineering inverse design, comparing it to conditional diffusion models and cGANs on EngiBench structural and thermal benchmarks. CFM outperforms the baselines in cumulative and final optimality gaps, mean volume‑fraction deviation, and throughput, achieving up to 66× faster sample generation with fewer network evaluations. The study demonstrates CFM’s effectiveness as a warm‑start generator for gradient‑based refinement in PDE‑constrained design problems.

By Juliana Felder, Milad Habibi, Soheyl Massoudi, Mark Fuge
diffusionefficiencybenchmarkssafety
More like this →