Gray Box

Gray-box modeling integrates known physical principles or structural information with data-driven techniques to create more robust and interpretable models than purely black-box approaches. Current research focuses on applying gray-box methods to diverse areas, including adversarial attacks on machine learning models (using techniques like surrogate models and gradient-based methods), optimization problems (developing generalized operators), and physical system identification (employing neural networks coupled with symbolic regression or physics-informed neural networks). This approach offers advantages in data efficiency, improved generalization, and enhanced model trustworthiness, impacting fields ranging from cybersecurity and AI safety to engineering and systems biology.