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Physics-Informed Neural Networks (PINN) have emerged as a formidable tool for addressing sophisticated computational physics challenges, offering an innovative approach to integrating physical laws directly into deep learning models. By incorporating the ...

In the middle and lower reaches of the Yangtze River Basin in China, a large amount of rice enters the heading and flowering stage during the hot summer following the plum rain season. The prolonged high temperature easily triggers widespread rice high-...

The performance of military supply chain networks (MSCNs) against disruptions is an important consideration for defense logistics decision making, and it is crucial to evaluate it scientifically and accurately. This paper highlights the problem from the ...

Most low-rank tensor approximations are NP-hard problems. In this paper, we introduce a novel concept: field-of-experts (FoE) filters guided tensor completion, which aims to integrate the strengths of the emerging tensor completion method and the ...

The effectiveness of military supply chain networks is an important reference for logistics decision-making, and it is crucial to evaluate it scientifically and accurately. This paper highlights the problem from the perspective of dynamic and discrete ...

Recently, two piecewise smooth models L0smoothing and relative total variation (RTV) have been proposed for feature/structure-preserving filtering. One is very efficient for tackling image with little texture patterns and the other has appearance ...