This research explores how Large Language Models can uncover hidden patterns and relationships in partial differential equations (PDEs), transforming how we approach engineering problems.

• Leverages LLMs to identify symbolic structures within PDEs that traditional methods might miss
• Bridges the gap between computational solutions and fundamental mathematical insights
• Offers a novel approach to understanding the underlying physics of engineering systems
• Demonstrates AI's potential to augment scientific discovery in mathematics

For engineering applications, this approach could revolutionize how we model complex systems by revealing fundamental relationships that drive physical phenomena, potentially leading to more efficient solution methods and deeper understanding of engineering challenges.

From Equations to Insights: Unraveling Symbolic Structures in PDEs with LLMs