Engineering

Porous media play a critical role in various industrial fields due to their complex pore networks and considerable specific surface areas. The transport and reaction phenomena within porous media are key factors influencing fundamental parameters such as energy storage efficiency, catalytic performance, and adsorption rates. To accurately describe these complex transport and reaction processes, solving parameterized partial differential equations (PDEs) is necessary. However, due to the complex structure of porous media, traditional methods, such as the finite element method (FEM), require substantial computational resources. There is an urgent need for innovative methods to accelerate the solution of parameterized PDEs in porous media. Researchers have developed a novel deep operator network, Porous-DeepONet, which can efficiently capture the complex features of porous media and thereby more precisely and effectively learn the solution operators, providing a robust alternative for sol...

Intelligent manufacturing is one of the most important strategies towards the upgradation of chemical industry. The deep learning modeling technology acts as a crucial role in this industry upgradation with its strong fitting and predicting ability. At the same time, such intelligent modeling technology is still challenged by the complexity of chemical processes, leading to limited generalization performances in practice. Therefore, the chemical industry calls for more interpretable and generalized deep learning modeling architectures. The LACG intelligent modeling architecture integrates three specially designed deep learning modules based on underlying logics to learn different driving forces of actual chemical processes In a recent research paper published in the journal of  Engineering , Prof. Weifeng Shen’s team at Chongqing University presents a novel interpretable deep learning modeling architecture for chemical processes, which is called light attention–convolution–gate rec...