Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

As climate change is exacerbated and existing resources are depleted, the need for
sustainable industries becomes ever so important. Aviation is not an exception. Despite the …

Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

The electrification of aircraft propulsive systems has been identified as one of the potential
solutions towards a lower carbon footprint in the aviation industry. However, there are still …

Statistical models can be used as surrogates of detailed simulation models. Their key
advantage is that they are evaluated at low computational cost which can remove …

The ability to accurately model and simulate chemical processes has been paramount to the
growing success and efficiency in process design and operation. These improvements …

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Evolutionary algorithms require large number of function evaluations to locate the global
optimum, making it computationally prohibitive on dealing with expensive problems …

Surrogate-based optimization has been used in aerodynamic shape optimization, but it has
been limited due to the curse of dimensionality. Although a large number of variables are …