Background and Motivation Understanding fluid motion is crucial for comprehension of our environment and our interactions with it in the field of physics. However, traditional fluid simulation methods face limitations in practical applications due to high computational demands. In recent years, physics-driven neural networks have emerged as a promi...

Study of Heterogeneous Hopfield Neural Networks: Dynamic Behavior Analysis Combining Adaptive Activation Functions and Memristors This study investigates the impact of nonlinear factors on the dynamic behavior of neural networks. Specifically, activation functions and memristors are commonly used as nonlinear factors to construct chaotic systems an...

Salient Object Detection in Low-Light RGB-T Scenarios by Mining Spatial-Frequency Cues Salient Object Detection (SOD) holds a significant position in the field of computer vision. Its main task is to identify the most visually attractive regions or objects in an image. Although SOD models have made certain progress in normal lighting environments o...

Heterogeneous Coexisting Attractors, Large-Scale Amplitude Control and Finite-Time Synchronization of Central Cyclic Memristive Neural Networks Academic Background Due to their memory and nonlinearity characteristics similar to brain synapses, memristors hold significant theoretical and practical importance in the study of chaotic dynamics in brain...

Adaptive Sampling Artificial-Actual Control for Non-Zero-Sum Games of Constrained Systems Background In modern industrial and scientific research fields, the rapid development of intelligent technology and control systems makes traditional control methods difficult to meet the strict requirements of ensuring system stability and minimizing energy c...