Topology optimization (TO) is a method of deriving an optimal design that satisfies a given
load and boundary conditions within a design domain. This method enables effective design …

Abstract 3D/4D printing offers significant flexibility in manufacturing complex structures with
a diverse range of mechanical responses, while also posing critical needs in tackling …

Mechanical metamaterials are artificial materials with unique global properties due to the
structural geometry and material composition of their unit cell. Typically, mechanical …

The energy harvesting capability of a graded metamaterial is maximised via reinforcement
learning (RL) under realistic excitations at the microscale. The metamaterial consists of a …

Mechanical metamaterials are regularly implemented in engineering applications due to
their unique properties derived from their structural geometry and material composition. This …

The advent of deep learning provides a promising opportunity to improve the efficiency of
topology optimization. However, existing methods make it difficult to achieve a balance …

Reinforcement learning algorithms can autonomously learn to search a design space for
high-performance solutions. However, modern engineering often entails the use of …

Power converters are pervasive in modern electronic component design. They can be found
in all electronic devices from household appliances and cellphone chargers to vehicles …

This paper explores the application of deep reinforcement learning for autonomously
designing noise-mitigating structures. Specifically, deep Q-and double deep Q-networks are …

In this study, topology optimization based on multi-labeled image data was conducted for a
multi-objective primary mirror to produce novel designs with varying design variables. The …