Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

Generative Adversarial Networks (GANs) have shown remarkable success in producing
realistic-looking images in the computer vision area. Recently, GAN-based techniques are …

Two-dimensional hydrodynamic models are computationally expensive. This drawback can
limit their application to solving problems requiring real-time predictions or several …

Fault diagnosis is the key procedure to ensure the stability and reliability of mechanical
equipment operation. Recent works show that deep learning-based methods outperform …

To solve the problems associated with a small sample size during intelligent road detection,
a virtual image set generation method for asphalt pavement cracks is proposed based on …

The rapid acquisition of high-fidelity flow field information is of great significance for
engineering applications such as multi-field coupling. Current research in flow field …

Modeling of wind farm wakes is of great importance for the optimal design and operation of
wind farms. In this work a surrogate modeling method for parametrized fluid flows is …

Using machine learning for pluvial flood prediction tasks has gained growing attention in the
past years. In particular, data-driven models using artificial neuronal networks show …

A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from
standard models are commonly used to build computer recognition algorithms for acquiring …