Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …

The disassembly complexity of end-of-life products increases continuously. Traditional
methods are facing difficulties in solving the decision-making and control problems of …

This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

Autonomous robots are increasingly served in real-world unstructured human environments
with complex long-horizon tasks, such as restaurant serving and office delivery. Task and …

Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …

The resilience of interdependent critical infrastructure systems (ICISs) is critical for the
functioning of society and the economy. ICISs such as power grids and telecommunication …

The global energy transition, driven by the imperative to mitigate climate change, demands
innovative solutions to address the technical, economic, and social challenges of …

Currently, a significant effort in the world research panorama is focused on finding efficient
solutions to a carbon-free energy supply, wave energy being one of the most promising …