Foundation models have revolutionized artificial intelligence by providing versatile tools adaptable to various tasks. However, these models often struggle with out-of-distribution predictions, producing outputs that are unrealistic or physically infeasible. To address this, researchers have introduced the concept of physics-guided foundation models (PGFMs). By incorporating general domain physical knowledge into these models, PGFMs aim to enhance their performance across a wide range of applications. This integration not only improves the models' accuracy but also ensures that their outputs adhere to physical laws, making them more reliable for real-world tasks. arxiv.org
The development of PGFMs represents a significant advancement in AI, bridging the gap between abstract model predictions and practical, real-world applications. By grounding AI outputs in established physical principles, PGFMs offer a more robust framework for tasks that require a deep understanding of the physical world. This approach not only enhances the models' predictive capabilities but also opens new avenues for their application in fields such as engineering, environmental science, and robotics. As AI continues to evolve, the integration of domain-specific knowledge like physics is poised to play a crucial role in developing more accurate and dependable AI systems. arxiv.org