Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology.
However, its broad adoption in industry is still hindered by high entry barriers of design for …

Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …

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 …

Neural networks, and more broadly, machine learning techniques, have been recently
exploited to accelerate topology optimization through data-driven training and image …

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 …

We present a two-scale topology optimization framework for the design of macroscopic
bodies with an optimized elastic response, which is achieved by means of a spatially-variant …

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 …

Abstract Physics-Informed Neural Networks (PINNs) have recently attracted exponentially
increasing attention in the field of computational mechanics. This paper proposes a novel …

Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …

We put forward a general machine learning-based topology optimization framework, which
greatly accelerates the design process of large-scale problems, without sacrifice in …