In marine applications, estimating velocity fields or other states from limited data are
important as it provides a reference for active control. In this work, we propose PVNet …

Optical frequency combs (OFCs) have evolved into one of the most active areas of
photonics, underpinning advancements in both fundamental science and commercial …

In the context of data-driven deep learning, employing multi-fidelity methods for swift and
precise wake field prediction is a novel attempt. Current Multi-Fidelity Neural Networks …

This study analyzed a recuperative burner system that is critical for energy efficiency and
pollutant reduction in the firing processes required in the manufacturing industries. We …

Recovering flow states from limited observations provides supports for flow control and
super-resolution. Advances in deep learning have made it possible to construct precise state …

Recovering full states from limited observations provides supports for active control of the
cavitation, preventing power loss due to cavitation erosion. Recent advances in deep …

Process-based hydrologic models are invaluable tools for understanding the terrestrial
water cycle and addressing modern water resources problems. However, many hydrologic …

The introduction of deep learning has resolved the high-cost issues associated with
traditional methods in handling complex aerodynamics problems and is commonly used for …

A deep-learning segmentation model was proposed to predict steady-state thermal fields.
The U-Net architecture was used in a reversed order, with the body segments serving as …

Recovering the full-state from limited observation data is crucial because it provides a
reliable reference for active control. Advances in deep learning technology further enable …