After a long period of biological evolution, natural creatures will inevitably evolve body
surfaces suitable for the living environment. The functions of natural biological surfaces are …

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 …

Turbulence is a complicated phenomenon because of its chaotic behavior with multiple
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …

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 …

In this paper, a novel deep learning technique, called multi-domain physics-informed neural
network (M-PINN), is presented to solve forward and inverse problems of steady-state heat …

In aerodynamic shape optimization, a high-fidelity (HF) simulation is generally more
accurate but more time-consuming than a low-fidelity (LF) simulation. To take advantage of …

Reinforcement learning is applied to the development of control strategies in order to reduce
skin friction drag in a fully developed turbulent channel flow at a low Reynolds number …

We propose an interpretable deep learning (DL) model that extracts physical features from
turbulence data. Based on a conditional generative adversarial network combined with a …

The encoder–decoder LSTM (long short term memory) recurrent neural network is proposed
to predict storm surge in Florida. Two types of hurricanes with six events are collected for …

A hybrid neural network based on Densely Connected Convolutional Networks (DenseNet),
Convolutional Long Short-Term Memory Neural Network (ConvLSTM), and Deconvolutional …