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Found 7 papers, 1 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).
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.
DropTrack -- automatic droplet tracking using deep learning for microfluidic applications
One fundamental analysis frequently desired in microfluidic experiments is counting and tracking the droplets.
Classifying Turbulent Environments via Machine Learning
The problem of classifying turbulent environments from partial observation is key for some theoretical and applied fields, from engineering to earth observation and astrophysics, e. g. to precondition searching of optimal control policies in different turbulent backgrounds, to predict the probability of rare events and/or to infer physical parameters labelling different turbulent set-ups.
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.
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.
