SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems
Keywords: surrogate models, multimodal text and timeseries models, language-interfaced regression
TL;DR: Augmenting surrogate regression models of complex systems simulations with natural language interfaces.
Abstract: Surrogate models are used to predict the behavior of complex energy systems that are too expensive to simulate with traditional numerical methods.
Our work introduces the use of language descriptions, which we call "system captions" or SysCaps, to interface with such surrogates.
We argue that interacting with surrogates through text, particularly natural language, makes these models more accessible for both experts and non-experts.
We introduce a lightweight multimodal text and timeseries regression model and a training pipeline that uses large language models (LLMs) to synthesize high-quality captions from simulation metadata.
Our experiments on two real-world simulators of buildings and wind farms show that our SysCaps-augmented surrogates have better accuracy on held-out systems than traditional methods while enjoying new generalization abilities, such as handling semantically related descriptions of the same test system.
Additional experiments also highlight the potential of SysCaps to unlock language-driven design space exploration and to regularize training through prompt augmentation.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 12317
Loading