Papers for

radio-frequency device designers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Bayesian shape design improved using example-based parameter choices

Example-driven Parametrisations for Bayesian Shape Optimisation

Abstract: Bayesian optimisation is the natural tool for shape design when objectives are expensive and non-differentiable, but it needs a compact yet expressive parameterisation of the search space. Hand-crafting one is a complex endeavour requiring domain expertise, and often yields implicit infeasible regions, artificial bounds, and coupled, unordered coordinates. We instead learn the parameterisation from a collection of existing designs, applying principal component analysis to the deformations between shapes. The result is a linear, interpretable search space in which the number of components explicitly trades expressivity against dimensionality. Across aerofoils, wings, and radio-frequency cavities, spanning 2D geometry to 3D aerodynamics and electromagnetics, we show improved sample efficiency and the ability to explore beyond the confines of hand-crafted baselines.

Thu 8 OctMachine Learning

The gist

Designing shapes like airplane wings or radio parts is hard because testing new shapes can take a lot of time and resources. Bayesian optimisation helps find good shapes, but it needs a simple way to describe all possible shapes to search effectively. The authors show how to create this description automatically by learning from a collection of existing shapes, making the search more efficient and flexible. This method works for various shapes in two and three dimensions and lets designers explore better shapes faster.

Open → 2610.11984v1