The transformative potential of machine learning for experiments in fluid mechanics
The field of machine learning (ML) has rapidly advanced the state of the art in many fields of
science and engineering, including experimental fluid dynamics, which is one of the original …
science and engineering, including experimental fluid dynamics, which is one of the original …
Machine learning-assisted low-dimensional electrocatalysts design for hydrogen evolution reaction
J Li, N Wu, J Zhang, HH Wu, K Pan, Y Wang, G Liu… - Nano-Micro Letters, 2023 - Springer
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …
Nevertheless, the conventional" trial and error" method for producing advanced …
Super-resolution analysis via machine learning: a survey for fluid flows
This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
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 …
Super-resolution generative adversarial networks of randomly-seeded fields
Reconstruction of field quantities from sparse measurements is a problem arising in a broad
spectrum of applications. This task is particularly challenging when the mapping between …
spectrum of applications. This task is particularly challenging when the mapping between …
Development of the Senseiver for efficient field reconstruction from sparse observations
The reconstruction of complex time-evolving fields from sensor observations is a grand
challenge. Frequently, sensors have extremely sparse coverage and low-resource …
challenge. Frequently, sensors have extremely sparse coverage and low-resource …
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization
We focus on a convolutional neural network (CNN), which has recently been utilized for fluid
flow analyses, from the perspective on the influence of various operations inside it by …
flow analyses, from the perspective on the influence of various operations inside it by …
Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach
M Lazzara, M Chevalier, M Colombo, JG Garcia… - Aerospace Science and …, 2022 - Elsevier
Surrogate modelling can alleviate the computational burden of design activities as they rely
on multiple evaluations of high-fidelity models. However, the learning task can be adversely …
on multiple evaluations of high-fidelity models. However, the learning task can be adversely …
Experimental velocity data estimation for imperfect particle images using machine learning
We propose a method using supervised machine learning to estimate velocity fields from
particle images having missing regions due to experimental limitations. As a first example, a …
particle images having missing regions due to experimental limitations. As a first example, a …