In recent years the fluid mechanics community has been intensely focused on pursuing
solutions to its long-standing open problems by exploiting the new machine learning (ML) …

The study of microswimmers' behavior, including their self-propulsion, interactions with the
environment, and collective phenomena, has received significant attention over the past few …

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 …

Sensing is one of the most fundamental tasks for the monitoring, forecasting, and control of
complex, spatio-temporal systems. In many applications, a limited number of sensors are …

Finite-time Lyapunov exponents (FTLEs) provide a powerful approach to compute time-
varying analogs of invariant manifolds in unsteady fluid flow fields. These manifolds are …

Machine learning algorithms offer a tool to boost mobility and flexibility of a synthetic
microswimmer, hence may help us design truly smart microrobots. In this work, we design a …

Pursuing a drifting target in a turbulent flow is an extremely difficult task whenever the
searcher has limited propulsion and maneuvering capabilities. Even in the case when the …

We employ Q learning, a variant of reinforcement learning, so that an active particle learns
by itself to navigate on the fastest path toward a target while experiencing external forces …

The phenomenon of collective navigation has received considerable interest in recent years.
A common line of thinking, backed by theoretical studies, is that collective navigation can …

We consider the problem of two active particles in 2D complex flows with the multi-objective
goals of minimizing both the dispersion rate and the control activation cost of the pair. We …