Geospatial artificial intelligence (GeoAI) in the integrated hydrological and fluvial systems modeling: Review of current applications and trends
This paper reviews the current GeoAI and machine learning applications in hydrological and
hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial …
hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial …
Unsupervised deep learning for super-resolution reconstruction of turbulence
Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows
have used supervised learning, which requires paired data for training. This limitation …
have used supervised learning, which requires paired data for training. This limitation …
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
We investigate the applicability of the machine learning based reduced order model (ML-
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …
Deep reinforcement learning for turbulent drag reduction in channel flows
We introduce a reinforcement learning (RL) environment to design and benchmark control
strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The …
strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The …
A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data
Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-
temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an …
temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an …
Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives
M Buzzicotti - Europhysics Letters, 2023 - iopscience.iop.org
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) …
solutions to its long-standing open problems by exploiting the new machine learning (ML) …
[HTML][HTML] Three-dimensional ESRGAN for super-resolution reconstruction of turbulent flows with tricubic interpolation-based transfer learning
Turbulence is a complicated phenomenon because of its chaotic behavior with multiple
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …
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, biofluids, the atmosphere …
related to the physics of dispersion and mixing in engineering, biofluids, the atmosphere …
Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks
Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …
Instability-wave prediction in hypersonic boundary layers with physics-informed neural operators
Fast and accurate prediction of the nonlinear evolution of instability waves in high-speed
boundary layers requires specialized numerical algorithms, and augmenting limited …
boundary layers requires specialized numerical algorithms, and augmenting limited …