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.

用于仿真建模的学习框架中，使用语言作为接口与底层系统相互联系。我们称系统的语言描述为 “System Caption” 或 “SysCap”。通过使用大型语言模型 (LLMs) 来合成高质量的描述，我们解决了自然语言 SysCaps 与仿真运行数据配对的缺乏。在我们的框架下，我们针对两个复杂能源系统的真实世界模拟器训练了多模态文本和时间序列回归模型。我们的实验表明，设计面向真实世界代理模型的语言接口具有与标准基线相当的准确性。我们以定性和定量的方式展示了 SysCaps 解锁了基于文本提示的代理模型和超越以往可能性的新的泛化能力。我们将发布生成的 SysCaps 数据集和支持后续研究的代码。