ModSolAgent: Automated Finite Element Code Generation for Abaqus via LLM-Based Agent

Finite element simulation and solution process represents a critical component in engineering analysis. While large language models (LLMs) have demonstrated remarkable capabilities in general-purpose code generation from textual descriptions, their application to generating structured and specialized finite element simulation scripts presents unique challenges. A key challenge is AI-generated hallucination, as these tasks require precise intent analysis, complex planning and reasoning, and strict adherence to logical consistency during execution. This often results in outputs that seem convincing but are ultimately erroneous. To address this challenge, we propose ModSolAgent, an LLM-based agent that generates Python scripts for Abaqus software to perform finite element modeling and solving tasks. ModSolAgent ensures the accuracy and logical coherence of code generation by leveraging a structured reasoning instruction, dynamic retrieval guidance template, and iterative verification generation. Experimental results demonstrate that LLMs augmented by ModSolAgent achieve an 83.3% success rate on real-world finite element simulation tasks, effectively meeting most Abaqus scripting requirements while significantly outperforming baseline models. To further enhance accessibility, we construct the AbqInstruct dataset by distilling knowledge from ModSolAgent to fine-tune the lightweight open-source models. Experiments show that fine-tuning on AbqInstruct leads to substantial performance improvements, with the lightweight model achieving proficiency across most finite element modeling and solving tasks. This work establishes a paradigm for integrating LLMs with specialized engineering software and providing novel insights for other structured, domain-specific code generation scenarios in industrial applications.