4 citations · 4 across the 2 of their papers we have counts for
4 papers
Exploiting locality and physical invariants to design effective Deep Reinforcement Learning control of the unstable falling liquid film
Vincent Belus, Jean Rabault, Jonathan Viquerat +3
Instabilities arise in a number of flow configurations. One such manifestation is the development of interfacial waves in multiphase flows, such as those observed in the falling li…
A geometrical summation method for the Riemann zêta function
Ulysse Reglade
In this paper, we introduce a geometrical summation method that makes the original Riemann series converge over the critical strip. This method gives an analytical function, that c…
Deep Reinforcement Learning achieves flow control of the 2D Karman Vortex Street
Jean Rabault, Ulysse Reglade, Nicolas Cerardi +2
The Karman Vortex Street has been investigated for over a century and offers a reference case for investigation of flow stability and control of high dimensionality, non-linear sys…
Artificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control
Jean Rabault, Miroslav Kuchta, Atle Jensen +2
We present the first application of an Artificial Neural Network trained through a Deep Reinforcement Learning agent to perform active flow control. It is shown that, in a 2D simul…