In recent years, the success of the Koopman operator in dynamical systems analysis has
also fueled the development of Koopman operator-based control frameworks. In order to …

This research gauges the ability of deep reinforcement learning (DRL) techniques to assist
the optimization and control of fluid mechanical systems. It relies on introducing single-step …

The control of complex systems is of critical importance in many branches of science,
engineering, and industry. Controlling an unsteady fluid flow is particularly important, as flow …

Abstract Model order reduction through the POD-Galerkin method can lead to dramatic
gains in terms of computational efficiency in solving physical problems. However, the …

Deep reinforcement learning (DRL) for fluidic pinball, three individually rotating cylinders in
the uniform flow arranged in an equilaterally triangular configuration, can learn the efficient …

In this work, we present an approach to reconstruct high-resolution flow velocity or scalar
fields from sparse particle-based measurements such as particle tracking velocimetry …

In a recent article, we presented a framework to control nonlinear partial differential
equations (PDEs) by means of Koopman operator based reduced models and concepts …

Vortex shedding is an important physical phenomenon observed across many spatial and
temporal scales in fluids. Previous experimental and theoretical studies have established a …

Dynamical systems are generally modeled using Partial Differential Equations (PDE). These
models are intricately linked to the way scientists observe the world and, as such, they are …

This thesis gauges the relevance of deep reinforcement learning (DRL) techniques for the
optimal control of fluid mechanical systems. Reinforcement learning (RL) is the process by …