Core Stability

Core stability, broadly defined as the robustness and reliability of a system or model's performance under various conditions, is a central theme across diverse scientific fields. Current research focuses on improving stability in machine learning models (e.g., through regularization techniques, modified optimization algorithms like AdamG, and analyses of Jacobian alignment), in inverse problems (using methods like Wasserstein gradient flows and data-dependent regularization), and in dynamical systems (by mitigating data-induced instability). Understanding and enhancing core stability is crucial for building reliable and trustworthy systems across applications ranging from medical imaging and AI to robotics and control systems.