---
format: "aidr-story-markdown/v1"
id: "aa178b575bb249be9aef3b157a357abf7c2548967e757b952ff93341f18a30e3"
canonical_url: "https://aidr.today/aa178b57?lang=en"
title: "When is a large language model a good calibration agent? Physics-labeled prompting for budgeted engineering simulator calibration"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-10-19T04:00:00.000Z"
category: "Research"
topics: ["llm"]
source_urls: ["https://arxiv.org/abs/2610.22379"]
summary: "A study examines whether large language models conditioned on physical diagnostic descriptions can propose informative parameter updates for calibrating expensive engineering simulators more efficiently than conventional derivative-free optimizers, testing the approach on a cylinder-wake Large-Eddy Simulation and a damped-oscillator inverse problem."
---

# When is a large language model a good calibration agent? Physics\-labeled prompting for budgeted engineering simulator calibration

> [Open the canonical story](<https://aidr.today/aa178b57?lang=en>)

**Published:** 2026-10-19T04:00:00.000Z
**Category:** Research
**Topics:** llm

## Summary

A study examines whether large language models conditioned on physical diagnostic descriptions can propose informative parameter updates for calibrating expensive engineering simulators more efficiently than conventional derivative\-free optimizers, testing the approach on a cylinder\-wake Large\-Eddy Simulation and a damped\-oscillator inverse problem\.

## Sources

- [Story source](<https://arxiv.org/abs/2610.22379>)