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LLM workflow ModiGen enhances Modelica code generation for physical systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ModiGen, a workflow utilizing large language models (LLMs) to generate Modelica code for simulating complex physical systems. Current LLMs struggle with this task, often producing non-functional code. ModiGen integrates supervised fine-tuning, graph retrieval-augmented generation, and feedback optimization to significantly improve the accuracy and reliability of Modelica component and test case generation, achieving notable gains in pass@1 metrics. AI

IMPACT Enhances LLM capabilities in specialized engineering domains, potentially accelerating development of complex physical system simulations.

RANK_REASON The cluster describes a research paper detailing a new method for code generation using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM workflow ModiGen enhances Modelica code generation for physical systems

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The cluster describes a research paper detailing a new method for code generation using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiahui Xiang, Tong Ye, Peiyu Liu, Yinan Zhang, Wenhai Wang ·

    ModiGen: A Large Language Model-Based Workflow for Multi-Task Modelica Code Generation

    arXiv:2503.18460v2 Announce Type: replace-cross Abstract: Modelica is a widely adopted language for simulating complex physical systems, yet effective model creation and optimization require substantial domain expertise. Although large language models (LLMs) have demonstrated pro…

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