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Automating data-driven modeling and analysis for engineering applications using large language model agents
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DOI：10.1016/j.knosys.2026.115989.png)
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Abstract
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• LLM agents automate end-to-end engineering modeling workflows for tabular regression tasks. • Multi-agent and ReAct pipelines are benchmarked on the OECD/NEA critical heat flux problem. • Agent-built deep ensembles match expert baselines and outperform the traditional CHF lookup table.
Keywords:
Large language model agents
Multi-agent systems
ReAct
Workflow automation
Critical heat flux
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