SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems

• URL: http://arxiv.org/abs/2405.19653v1
• Date: Thu, 30 May 2024 03:12:04 GMT
• Title: SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems
• Authors: Patrick Emami, Zhaonan Li, Saumya Sinha, Truc Nguyen,
• Abstract summary: We introduce a learning framework for surrogate modeling where language is used to interface with the underlying system being simulated. We call a language description of a system a "system caption", or SysCap. We train multimodal text and timeseries regression models for two real-world simulators of complex energy systems.
• Score: 4.049850026698639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven simulation surrogates help computational scientists study complex systems. They can also help inform impactful policy decisions. We introduce a learning framework for surrogate modeling where language is used to interface with the underlying system being simulated. We call a language description of a system a "system caption", or SysCap. To address the lack of datasets of paired natural language SysCaps and simulation runs, we use large language models (LLMs) to synthesize high-quality captions. Using our framework, we train multimodal text and timeseries regression models for two real-world simulators of complex energy systems. Our experiments demonstrate the feasibility of designing language interfaces for real-world surrogate models at comparable accuracy to standard baselines. We qualitatively and quantitatively show that SysCaps unlock text-prompt-style surrogate modeling and new generalization abilities beyond what was previously possible. We will release the generated SysCaps datasets and our code to support follow-on studies.

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