The availability of large datasets has boosted the application of machine learning in many
fields and is now starting to shape active-matter research as well. Machine learning …

Abstract Machine learning (ML) has revolutionized robotics by enhancing perception,
adaptability, decision-making and more, enabling robots to work in complex scenarios …

The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and
weather prediction, hinge on the choice of turbulence models. The abundance of data from …

We introduce a reinforcement learning (RL) environment to design and benchmark control
strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The …

Turbulent flow models are critical for applications such as aircraft design, weather
forecasting and climate prediction. Existing models are largely based on physical insight …

Swimming microorganisms switch between locomotory gaits to enable complex navigation
strategies such as run-and-tumble to explore their environments and search for specific …

In recent years, artificial neural networks (ANNs) and deep learning have become
increasingly popular across a wide range of scientific and technical fields, including fluid …

Machine learning (ML), a subset of artificial intelligence, refers to methods that have the
ability to “learn” from experience, enabling them to carry out designated tasks. Examples of …

Thermal convection is ubiquitous in nature as well as in many industrial applications. The
identification of effective control strategies to, eg suppress or enhance the convective heat …

Efficient point-to-point navigation in the presence of a background flow field is important for
robotic applications such as ocean surveying. In such applications, robots may only have …