Stability Analysis

Stability analysis investigates the robustness and reliability of systems, encompassing diverse fields from dynamical systems and control theory to machine learning and AI. Current research focuses on developing and applying novel methods for assessing stability, including physics-informed neural networks, Lyapunov functions, and spectral analysis techniques, often tailored to specific model architectures like recurrent neural networks, convolutional neural networks, and graph neural networks. These advancements are crucial for improving the safety and performance of complex systems, ranging from robotic control and power grids to the development of robust and reliable AI models.