Taming Lagrangian chaos with multi-objective reinforcement learning

Calascibetta, Chiara; Biferale, Luca; Borra, Francesco; Celani, Antonio; Cencini, Massimo

doi:10.1140/epje/s10189-023-00271-0

Eur. Phys. J. E

2023

DOI: 10.1140/epje/s10189-023-00271-0

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Taming Lagrangian chaos with multi-objective reinforcement learning

Chiara Calascibetta

Luca Biferale

Francesco Borra

et al.

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Cited by 4 publications

(1 citation statement)

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“…In complex flows as well, there have been numerous positive outcomes in nearly all testing scenarios, varying from control problems as single and multi-agents navigation in complex environments [7][8][9][10][11][12][13][14][15], to turbulent control and drag reduction [16][17][18][19][20][21], up to data assimilation problems [22][23][24][25][26][27][28][29][30][31][32][33] to cite few of them. However, applications in fluids are still in their infancy, and the majority of cases are either conducted on highly idealized setups or only showing preliminary results on more realistic conditions.…”

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confidence: 99%

Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives

Buzzicotti

2023

EPL

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In recent years the fluid mechanics community has been intensely focused on pursuing solutions to its long-standing open problems by exploiting the new machine learning, (ML), approaches. The exchange between ML and fluid mechanics is bringing important paybacks in both directions. The first is benefiting from new physics-inspired ML methods and a scientific playground to perform quantitative benchmarks, whilst the latter has been open to a large set of new tools inherently well suited to deal with big data, flexible in scope, and capable of revealing unknown correlations. A special case is the problem of modeling missing information of partially observable systems. The aim of this paper is to review some of the ML algorithms that are playing an important role in the current developments in this field, to uncover potential avenues, and to discuss the open challenges for applications to fluid mechanics.

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confidence: 99%

Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives

Buzzicotti

2023

EPL

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Optimal tracking strategies in a turbulent flow

Calascibetta,

Biferale,

Borra

et al. 2023

Commun Phys

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Pursuing a drifting target in a turbulent flow is an extremely difficult task whenever the searcher has limited propulsion and maneuvering capabilities. Even in the case when the relative distance between pursuer and target stays below the turbulent dissipative scale, the chaotic nature of the trajectory of the target represents a formidable challenge. Here, we show how to successfully apply optimal control theory to find navigation strategies that overcome chaotic dispersion and allow the searcher to reach the target in a minimal time. We contrast the results of optimal control – which requires perfect observability and full knowledge of the dynamics of the environment – with heuristic algorithms that are reactive – relying on local, instantaneous information about the flow. While the latter display worse performances, optimally controlled pursuers can track the target for times much longer than the typical inverse Lyapunov exponent and are considerably more robust.

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Optimal navigation of a smart active particle: directional and distance sensing

Putzke,

Stark

2023

Eur. Phys. J. E

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We employ Q learning, a variant of reinforcement learning, so that an active particle learns by itself to navigate on the fastest path toward a target while experiencing external forces and flow fields. As state variables, we use the distance and direction toward the target, and as action variables the active particle can choose a new orientation along which it moves with constant velocity. We explicitly investigate optimal navigation in a potential barrier/well and a uniform/ Poiseuille/swirling flow field. We show that Q learning is able to identify the fastest path and discuss the results. We also demonstrate that Q learning and applying the learned policy works when the particle orientation experiences thermal noise. However, the successful outcome strongly depends on the specific problem and the strength of noise. Graphical abstract

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Taming Lagrangian chaos with multi-objective reinforcement learning

Cited by 4 publications

References 28 publications

Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives

Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives

Optimal tracking strategies in a turbulent flow

Optimal navigation of a smart active particle: directional and distance sensing

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