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Publications

  1. T. Redaelli, F. Candelier, R. Mehaddi, B. Mehlig (2022) Unsteady and inertial dynamics of a small active particle in a fluid, Phys. Rev. Fluids 7, 044304 DOI:10.1103/PhysRevFluids.7.044304, arXiv:2105.01408

  2. A. Loisy, C. Eloy (2022) Searching for a source without gradients: how good is infotaxis and how to beat it, Proc. R. Soc. A. 478, 20220118. DOI:10.1098/rspa.2022.0118, arXiv:2112.10861

  3. A. Loisy, C. Eloy (2022) OTTO: A Python package to simulate, solve and visualize the source-tracking POMDP, J. Open Source Softw. 7(74), 4266. DOI:10.21105/joss.04266

  4. R. Monthiller, A. Loisy, M.A.R. Koehl, B. Favier, C. Eloy (2022) Surfing on turbulence: A strategy for planktonic navigation, Phys. Rev. Lett. 129, 064502. DOI:10.1103/PhysRevLett.129.064502, arXiv:2110.10409

  5. M. Geiger, C. Eloy, M. Wyart (2021) How memory architecture affects learning in a simple POMDP: the two-hypothesis testing problem, arXiv:2106.08849

  6. A. Loisy, R. A. Heinonen (2023) Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark, Eur. Phys. J. E 46, 17. DOI:10.1140/epje/s10189-023-00277-8, arXiv:2302.00706

  7. T. Redaelli, F. Candelier, R. Mehaddi, C. Eloy, B. Mehlig (2023) Hydrodynamic force on a small squirmer moving with a time-dependent velocity at small Reynolds numbers, J. Fluid Mech. 973, A11. DOI:10.1017/jfm.2023.650, arXiv:2209.08138

  8. C. Eloy (2024) Hydrodynamics of flow sensing in plankton, Eur. Phys. J. Spec. Top. DOI:10.1140/epjs/s11734-024-01252-w, arXiv:2411.1731

  9. S. Mecanna, A. Loisy, C. Eloy (2024) Applying reinforcement learning to navigation in partially observable flows, Seventeenth European Workshop on Reinforcement Learning. OpenReview

  10. S. Mecanna, A. Loisy, C. Eloy (2025) A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows, Eur. Phys. J. E, 48, 58. DOI:10.1140/epje/s10189-025-00522-2, arXiv:2505.05525

  11. M. H. DiBenedetto, R. Monthiller, C. Eloy, L. S. Mullineaux (2025) Plankton active response to turbulence enables efficient transport, J. Exp. Biol., 228 DOI:10.1242/jeb.251123

Open-source codes

  • sheld0n : A code that enables advection of active particles in flows.

  • otto : A Python package to visualize, evaluate and learn strategies for odor-based searches.

  • RLfl0w : A reinforcement learning code to optimize the trajectories of active particles in complex flows

Serious games

These games have been developped as part of student projects.