By VADM David Lewis (USN, Ret.), President of the American Society of Naval Engineers

Over the past few years, advancements in artificial intelligence have driven considerable progress in hydrodynamic design, particularly in hull optimization workflows. A growing body of peer‑reviewed and preprint work from 2024 to 2026 shows that AI’s role is shifting from being “possible in principle” to delivering measurable, task‑level performance gains in the field.

Quantifying Performance Gains with Surrogate and Multi‑Fidelity Models

Researchers have begun quantifying the concrete benefits of surrogate and multi-fidelity workflows, which help reduce or replace expensive computational fluid dynamics (CFD) sampling. Surrogate models, constructed using neural networks, co‑kriging, and hybrid architectures, are now capable of standing in for some CFD calls. This approach accelerates early-stage exploration and significantly trims simulation budgets that were previously considered standard practice. (ScienceDirect)

Integrating Deep Learning into the CFD Pipeline

More recent developments integrate deep learning techniques directly into the CFD workflow. Deep neural networks (DNNs) are being trained to reconstruct flow fields or total resistance surfaces, thereby avoiding costly evaluations of hull variants that have not yet been simulated. These methods promise order‑of‑magnitude savings in computing time for resistance prediction and design iterations. (ScienceDirect)

Generative Models for Hull Geometry Optimization

On the generative front, diffusion-based and variational autoencoder models are being designed to generate low-resistance hull geometries that meet specified constraints. Academic papers and preprints demonstrate that these approaches can lead to substantial reductions in computed resistance, sometimes exceeding 25–30% compared to parent hulls, and do so without requiring retraining for each new problem. (arXiv)

Reinforcement Learning in Multi‑Objective Design

Reinforcement learning is also being applied to the multi-objective hull trade-off space. RL-style agents are used to guide parameter updates and balance resistance against other performance criteria, showing faster convergence to optimal design solutions than traditional search heuristics in several studies. (Taylor & Francis Online)

Impact on Practical Design Workflows

These technological advances are no longer just theoretical. By combining surrogate and multi‑fidelity models with physics‑aware constraints and intelligent search heuristics, researchers have reported measurable program improvements. These include fewer high-fidelity CFD calls per optimization cycle, shorter end-to-end design loops, and a higher first-pass design success rate when machine learning components are integrated into design automation systems. (ScienceDirect)

Conclusion

In summary, the field is moving beyond the notion that “AI might help someday” to delivering practical, quantifiable impacts on hull design search efficiency and on predicted versus validated performance outcomes.

Main image caption:  USS Kingsville (LCS 36) sails toward Naval Surface Warfare Center, Port Hueneme Division (NSWC PHD) in California on a recent morning as a flock of brown pelicans flies low over the Pacific Ocean. Homeported in San Diego, LCS 36 is an Independence-variant littoral combat ship that was commissioned in 2024. The arrival marked the ship’s first visit to NSWC PHD, where it underwent a Combat Systems Assessment Team event to ensure combat readiness. Designed to operate closer to shore than guided-missile destroyers and cruisers, USS Kingsville is outfitted for mine countermeasures missions. (U.S. Navy photo by Eric Parsons)