Mathematical Reasoning Enhanced LLM for Formula Derivation: A Case Study on Fiber NLI Modellin
Recent advances in large language models (LLMs) have demonstrated strong capabilities in code generation and text synthesis, yet their potential for symbolic physical reasoning in domain-specific scientific problems remains underexplored. We present a mathematical reasoning enhanced generative AI approach for optical communication formula derivation, focusing on the fiber nonlinear interference modelling. By guiding an LLM with structured prompts, we successfully reconstructed the known closed-form ISRS GN expressions and further derived a novel approximation tailored for multi-span C and C+L band transmissions. Numerical validations show that the LLM-derived model produces central-channel GSNRs nearly identical to baseline models, with mean absolute error across all channels and spans below 0.109 dB, demonstrating both physical consistency and practical accuracy.
Code (0)
등록된 구현이 없습니다.
Tasks
Mathematical ReasoningCode GenerationSimilar Papers 제목 키워드 기반
Decoupling Knowledge and Reasoning in Transformers: A Modular Architecture with Generalized Cross-Attention
Transformers have achieved remarkable success across diverse domains, but their monolithic architecture presents challenges in interpretability, adaptability, and scalability. This paper introduces a novel modular Transf…
RetrievalMathematical Derivation Graphs: A Task for Summarizing Equation Dependencies in STEM Manuscripts
Recent advances in natural language processing (NLP), particularly with the emergence of large language models (LLMs), have significantly enhanced the field of textual analysis. However, while these developments have yie…
ArticlesDAG-Math: Graph-of-Thought Guided Mathematical Reasoning in LLMs
Large Language Models (LLMs) demonstrate strong performance on mathematical problems when prompted with Chain-of-Thought (CoT), yet it remains unclear whether this success stems from search, rote procedures, or rule-cons…
Mathematical ReasoningAI Descartes: Combining Data and Theory for Derivable Scientific Discovery
Scientists have long aimed to discover meaningful formulae which accurately describe experimental data. A common approach is to manually create mathematical models of natural phenomena using domain knowledge, and then fi…
Automated Theorem ProvingBIG-bench Machine LearningEquation DiscoveryForm+3On the Derivation of Tightly-Coupled LiDAR-Inertial Odometry with VoxelMap
This note presents a concise mathematical formulation of tightly-coupled LiDAR-Inertial Odometry within an iterated error-state Kalman filter framework using a VoxelMap representation. Rather than proposing a new algorit…