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 …

Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …

Deep learning surrogate models are being increasingly used in accelerating scientific
simulations as a replacement for costly conventional numerical techniques. However, their …

The reconstruction of the self-luminous image of a flame through deep learning can inform
research on the characteristics of combustion of a scramjet. In this study, the authors …

The rapid and accurate reconstruction of the supersonic combustor flow field is of great
significance for sensing and predicting the combustion state. Existing deep learning …

Flame prediction using deep learning technology could promote the research and
development of flame propagation in scramjet combustors. A data-driven prediction model is …

The computational cost of traditional gradient-based topology optimization is amplified for
multidisciplinary design optimization (MDO) problems, most notably when coupling between …

One challenge in the design of frequency-selective surface (FSS) is that the designed results
are difficult to meet the accuracy demand of various physical properties simultaneously, part …

This paper presents a methodological framework for training, self-optimising, and self-
organising surrogate models to approximate and speed up multiobjective optimisation of …

Residual stress induced during and as a result of manufacturing processes can have a
significant impact on the later stages of manufacturing (eg, machining), and in-service …