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Correction to : Eur. Phys. J. E (2023) 46:27
In this article the annotations have been missing for Fig. 1; the figure should have appeared as shown below. The original article has been corrected.
Overview of our multi-agent DRL approach to drag reduction. The simulation domain is shown on the left. The agents are organized in a grid \(N_{\mathrm{{CTRLx}}} \times N_{\mathrm{{CTRLz}}}\). Each agents observes the velocity fluctuations in the streamwise (u′) and wall-normal (v′) direction. The reward is the percentage variation of the wall-shear stress \(\tau _w\). Based on the state, each agent acts by imposing a wall-normal velocity v at the wall
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Guastoni, L., Rabault, J., Schlatter, P. et al. Correction to: Deep reinforcement learning for turbulent drag reduction in channel flows. Eur. Phys. J. E 46, 51 (2023). https://doi.org/10.1140/epje/s10189-023-00304-8
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DOI: https://doi.org/10.1140/epje/s10189-023-00304-8