The question of how methods from the field of artificial intelligence can help improve the
conventional frameworks for topology optimisation has received increasing attention over …

Automated design synthesis has the potential to revolutionize the modern engineering
design process and improve access to highly optimized and customized products across …

Metamaterials are emerging as a new paradigmatic material system to render
unprecedented and tailorable properties for a wide variety of engineering applications …

Deep learning has recently been applied to various research areas of design optimization.
This study presents the need and effectiveness of adopting deep learning for generative …

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Engineered materials are ubiquitous throughout society and are critical to the development
of modern technology, yet many current material systems are inexorably tied to widespread …

In topology optimization using deep learning, the load and boundary conditions represented
as vectors or sparse matrices often miss the opportunity to encode a rich view of the design …

Identifying the key microstructure representations is crucial for computational materials
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …

Structural topology optimization, which aims to find the optimal physical structure that
maximizes mechanical performance, is vital in engineering design applications in …

Driven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial
intelligence (AI) has empowered smart manufacturing and digital transformation. AI …