As Artificial Intelligence (AI) becomes deeply integrated into the physical world, ensuring verifiable, robust, and safe decision-making and control is paramount. Purely data-driven methods, while successful in perception tasks, often suffer from a “black box” nature when deployed in safety-critical environments, leading to insufficient generalization and potential hazards. Therefore, the paradigm of trustworthy decision-making and control guided by physical world information has emerged. It integrates physical laws, system dynamics, and environmental constraints into AI models, either as regularization terms in the loss function, embedded within the network architecture, or through hybrid models. It allows AI to simultaneously learn from data and consistently follow physical laws. This paper compares trustworthy computing guided by physical knowledge with traditional data-driven methods, focusing on two representative paradigms: Physics-Informed Neural Networks (PINNs) and Physics-Informed Reinforcement Learning (PIRL). We present their core concepts and integration mechanisms, embedding physical laws via loss regularization or network architecture, and incorporating physics into the learning or control process. This paper reveals the significant improvements of the trustworthy computing in interpretability, robustness, and safety, providing a new path from “black box” to “physically interpretability” transformation for safety-critical domains such as autonomous driving, robot collaboration, and industrial process control. Understanding the differences and connections within this paradigm is essential for advancing AI technologies that can serve human society safely and reliably.