Therefore, we propose an improved physics-informed neural networks (PINNs) based on deep separable convolutional network (DSCN) and attention mechanism for the RUL estimation of rolling bearings. Specifically, a deep separable convolutional network is introduced for feature extraction, which directly ...
Therefore, we propose an improved physics-informed neural networks (PINNs) based on deep separable convolutional network (DSCN) and attention mechanism for the RUL estimation of rolling bearings. Specifically, a deep separable convolutional network is introduced for feature extraction, which directly ...
traveling waves; solitons; peakons; compactons; separable gaussian neural networks; physics-informed neural networks 1. Introduction Physics-informed Neural Networks (PINNs) [1,2] have emerged as a promising data-driven approach to solving partial differential equations (PDEs) by synthesizing data and...
traveling waves; solitons; peakons; compactons; separable gaussian neural networks; physics-informed neural networks 1. Introduction Physics-informed Neural Networks (PINNs) [1,2] have emerged as a promising data-driven approach to solving partial differential equations (PDEs) by synthesizing data and...
Informed Consent Statement Not applicable. Data Availability Statement Data sharing not applicable. Conflicts of Interest The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: FPGA Field-Programmable Gate Array OI-DSCNN Odor Identification with...