61 citations · 64 across the 5 of their papers we have counts for
5 papers
Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
Shahroz Khan, Zahid Masood, Muhammad Usama +4
In this work, we propose a set of physics-informed geometric operators (GOs) to enrich the geometric data provided for training surrogate/discriminative models, dimension reduction…
Generative VS non-Generative Models in Engineering Shape Optimization
Muhammad Usama, Zahid Masood, Shahroz Khan +2
In this work, we perform a systematic comparison of the effectiveness and efficiency of generative and non-generative models in constructing design spaces for novel and efficient d…
How does agency impact human-AI collaborative design space exploration? A case study on ship design with deep generative models
Shahroz Khan, Panagiotis Kaklis, Kosa Goucher-Lambert
Typical parametric approaches restrict the exploration of diverse designs by generating variations based on a baseline design. In contrast, generative models provide a solution by…
Accelerating Simulation-Driven Optimisation of Marine Propellers Using Shape-Supervised Dimension Reduction
Shahroz Khan, Stefano Gaggero, Panagiotis Kaklis +2
Simulation-driven shape optimisation (SDSO) of marine propellers is often obstructed by high-dimensional design spaces stemming from its complex geometry and baseline parameterisat…
ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional generative model
Shahroz Khan, Kosa Goucher-Lambert, Konstantinos Kostas +1
In this work, we introduce ShipHullGAN, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs) for the versatile representation and gen…