On Languaging a Simulation Engine

TL;DR

This paper introduces Lang2Sim, a framework using functionalized language models to transform natural language descriptions into executable simulation setups, enabling interactive and precise simulation customization.

Contribution

It proposes a novel language-to-simulation framework that interprets simulation scenarios through functionalized language models, moving beyond traditional coding methods.

Findings

Enables interactive navigation of simulation setup via language models.

Interprets simulators as invariant tools with variable inputs.

Balances memory and information completeness across model types.

Abstract

Language model intelligence is revolutionizing the way we program materials simulations. However, the diversity of simulation scenarios renders it challenging to precisely transform human language into a tailored simulator. Here, using three functionalized types of language model, we propose a language-to-simulation (Lang2Sim) framework that enables interactive navigation on languaging a simulation engine, by taking a scenario instance of water sorption in porous matrices. Unlike line-by-line coding of a target simulator, the language models interpret each simulator as an assembly of invariant tool function and its variant input-output pair. Lang2Sim enables the precise transform of textual description by functionalizing and sequentializing the language models of, respectively, rationalizing the tool categorization, customizing its input-output combinations, and distilling the simulator input into executable format. Importantly, depending on its functionalized type, each language model features a distinct processing of chat history to best balance its memory limit and information completeness, thus leveraging the model intelligence to unstructured nature of human request. Overall, this work establishes language model as an intelligent platform to unlock the era of languaging a simulation engine.