Search Results for author:
Found 11 papers, 2 papers with code
Synthetic Lagrangian Turbulence by Generative Diffusion Models
Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, bio-fluids, atmosphere, oceans, and astrophysics.
Generative Adversarial Networks to infer velocity components in rotating turbulent flows
We perform a systematic quantitative benchmark of point-wise and statistical reconstruction capabilities of the linear Extended Proper Orthogonal Decomposition (EPOD) method, a non-linear Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN).
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning
We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair.
Multi-scale data reconstruction of turbulent rotating flows with Gappy POD, Extended POD and Generative Adversarial Networks
Different gap sizes and gap geometries are investigated in order to assess the importance of coherency and multi-scale properties of the missing information.
Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number
We consider a model of two competing microswimming agents engaged in a pursue-evasion task within a low-Reynolds-number environment.
Optimal control of point-to-point navigation in turbulent time-dependent flows using Reinforcement Learning
In this case, we optimize a linear combination between the total navigation time and the total time the engine is switched off.
Cascade or not cascade? Energy transfer and elastic effects in active nematics
We numerically study the multi-scale properties of a 2d active gel to address the energy transfer mechanism.
Soft Condensed Matter
Structure and Isotropy of Lattice Pressure Tensors for Multi-range Potentials
We systematically analyze the tensorial structure of the lattice pressure tensors for a class of multi-phase lattice Boltzmann models (LBM) with multi-range interactions.
Statistical Mechanics Cellular Automata and Lattice Gases Fluid Dynamics
Controlling Rayleigh-Bénard convection via Reinforcement Learning
Thermal convection is ubiquitous in nature as well as in many industrial applications.
Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning
To find the path that minimizes the time to navigate between two given points in a fluid flow is known as Zermelo's problem.
Flow Navigation by Smart Microswimmers via Reinforcement Learning
Smart active particles can acquire some limited knowledge of the fluid environment from simple mechanical cues and exert a control on their preferred steering direction.
