#4750 Applying LLMs to "Discover" and "Improve" Based on Classical Mechanics KG
#4750 Applying LLMs to "Discover" and "Improve" Based on Classical Mechanics KG #4750 Once I have a well-populated Classical Mechanics knowledge graph based on the text book, how can I use LLMs to 1) "discover" new concepts and principles, and 2) improve user-specified mechanical systems? Connecting a structured Classical Mechanics Knowledge Graph (KG) with an LLM gives you a dual neuro-symbolic engine: the graph acts as an exact, deterministic semantic anchor (conserved quantities, governing equations, assumptions, degrees of freedom), while the LLM acts as an analogical reasoner and hypothesis generator. 1. "Discovering" New Concepts and Principles Because Classical Mechanics is fundamentally closed at macroscopic scales, "discovery" here translates to uncovering latent symmetries, cross-domain isomorphisms, and novel invariant quantities that are not explicitly stated in textbook indices. A. Link Prediction via Isomorphic Su...