Parametric Model Embedding

Abstract

Methodologies for reducing the design-space dimensionality in shape optimization have been recently developed based on unsupervised machine learning methods. These methods provide reduced dimensionality representations of the design space, capable of maintaining a certain degree of the original design variability. Nevertheless, they usually do not allow to use directly the original parameterization method, representing a limitation to their widespread application in the industrial field, where the design parameters often pertain to well-established parametric models, e.g. CAD (computer-aided design) models. This work presents how to embed the parametric-model original parameters in a reduced-dimensionality representation of the design space. The method, which takes advantage from the definition of a newly-introduced generalized feature space, is demonstrated, as a proof of concept, for the reparameterization of 2D Bezier curves and 3D free-form deformation design spaces and the consequent solution of simulation-driven design optimization problems of a subsonic airfoil and a naval destroyer in calm water, respectively.

arXiv commentPaper submitted to Computer Methods in Applied Mechanics and Engineering

References in corpus (4)

• Machine Learning in Aerodynamic Shape Optimization
• Mapping the global design space of nanophotonic components using machine learning pattern recognition
• Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems
• Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version

Cited by in corpus (4)

• Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization
• A Scoping Review on Simulation-based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps
• Airfoil Optimization using Design-by-Morphing
• Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization