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

At the time of designing structures up to date, the density and magnitude of the load have
increased, and the requirements for regulation have also become more stringent. To ensure …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

Abstract We present a Physics-Informed Neural Network (PINN) to simulate the
thermochemical evolution of a composite material on a tool undergoing cure in an …

Deep reinforcement learning (DRL) has been applied to a variety of problems during the
past decade and has provided effective control strategies in high-dimensional and non …

The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …

In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …

Due to the advances in the digitalization process of the manufacturing industry and the
resulting available data, there is tremendous progress and large interest in integrating …