Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …

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 …

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 …

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 …

Modern air vehicles perform a wide range of operations, including transportation, defense,
surveillance, and rescue. These aircraft can fly in calm conditions but avoid operations in …

The reconstruction of complex time-evolving fields from sensor observations is a grand
challenge. Frequently, sensors have extremely sparse coverage and low-resource …

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 …

Reconstruction of field quantities from sparse measurements is a problem arising in a broad
spectrum of applications. This task is particularly challenging when the map** between …

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 …

High-dimensional dynamical systems often require computationally intensive physics-based
simulations, making full physical space data assimilation impractical. Latent data …