Rational Neural Network

Rational neural networks (RNNs) are emerging as powerful tools for various scientific and engineering applications, primarily focusing on improving the accuracy and robustness of existing methods. Current research emphasizes using RNNs to enhance existing algorithms, such as weighted essentially non-oscillatory schemes in fluid dynamics and reinforcement learning approaches for adaptive control, by leveraging their ability to represent complex functions and handle discontinuities effectively. This approach addresses challenges like mitigating numerical dissipation, improving the stability-plasticity dilemma in continual learning, and ensuring safety and stability in control systems. The resulting improvements in accuracy, efficiency, and robustness have significant implications for diverse fields, including computational fluid dynamics, transportation optimization, and robotics.