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researcher

O. Lehmkuhl

12 papers hereh-index 8403 citations20 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author9
  • last author3

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • physics.flu-dyn10
  • cs.CE2
same name
  • O. Lehmkuhl — 7 papers, h 4
  • O. Lehmkuhl — 3 papers, h 34
  • O. Lehmkuhl — 2 papers, h 2
  • O. Lehmkuhl — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedActive flow control of a turbulent separation bubble through deep reinforcement learning

13 citations · 27 across the 9 of their papers we have counts for

collaborators
Showing physics.flu-dynShow all

3 papers · 1 filter

physics.flu-dyn2025

SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms

Maochao Xiao, Yuning Wang, Felix Rodach +15

Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and disco…

physics.flu-dyn2025

Deep-reinforcement-learning-based separation control in a two-dimensional airfoil

Xavier Garcia, Arnau Miró, Pol Suárez +5

The aim of this study is to discover new active-flow-control (AFC) techniques for separation mitigation in a two-dimensional NACA 0012 airfoil at a Reynolds number of 3000. To find…

physics.flu-dyn2024

On Deep-Learning-Based Closures for Algebraic Surrogate Models of Turbulent Flows

Benet Eiximeno, Marcial Sanchís-Agudo, Arnau Miró +3

A deep-learning-based closure model to address energy loss in low-dimensional surrogate models based on proper-orthogonal-decomposition (POD) modes is introduced. Using a transform…

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