activity
20182022
most citedArtificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control

572 citations · 649 across the 5 of their papers we have counts for

collaborators

5 papers

quant-ph2022★ 71 cited

One hundred second bit-flip time in a two-photon dissipative oscillator

C. Berdou, A. Murani, U. Reglade +19

Current implementations of quantum bits (qubits) continue to undergo too many errors to be scaled into useful quantum machines. An emerging strategy is to encode quantum informatio…

physics.flu-dyn2019★ 4 cited

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…

math.GM2019

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…

physics.flu-dyn2018★ 2 cited

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…

physics.flu-dyn2018★ 572 cited

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…