Physical Prior

Physical priors, representing prior knowledge about the physical world, are increasingly integrated into machine learning models to improve efficiency, accuracy, and generalizability. Current research focuses on incorporating these priors into various model architectures, including Bayesian neural networks, Gaussian processes, and diffusion models, often within frameworks of cooperative learning or physics-informed Bayesian optimization. This approach enhances model performance across diverse applications, such as robotics, image processing, and scientific simulations, by leveraging physical constraints and reducing reliance on massive datasets. The resulting models are not only more accurate but also more robust and interpretable, bridging the gap between data-driven and physics-based approaches.