Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

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 …

Efficiently navigating a superpressure balloon in the stratosphere requires the integration of
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data
from experiments, field measurements, and large-scale simulations at multiple …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

Activity and autonomous motion are fundamental in living and engineering systems. This
has stimulated the new field of'active matter'in recent years, which focuses on the physical …

This paper focuses on the active flow control of a computational fluid dynamics simulation
over a range of Reynolds numbers using deep reinforcement learning (DRL). More …

To realize the potential of autonomous underwater robots that scale up our observational
capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be …

This book has been drafted based on my lectures and seminars from recent years for
postgraduate students at Auckland University of Technology (AUT), New Zealand. We have …