SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems

2024-05-30

arXiv.org

AI Summary

To address the poor accessibility and weak generalization of surrogate models for complex energy systems, this paper proposes SysCaps—the first natural-language interface framework for simulation systems. Methodologically: (1) it formalizes system descriptions as unified semantic inputs, termed SysCaps; (2) it develops a lightweight multimodal text–time-series regression model; and (3) it introduces an LLM-driven pipeline to synthesize high-quality SysCaps from simulation metadata. Contributions include: the first demonstration of semantic-level generalization across heterogeneous energy systems (e.g., buildings and wind farms), outperforming conventional surrogates in accuracy; support for language-guided design-space exploration and prompt-augmented regularization; and significantly improved generalization to unseen systems and training stability. This work establishes a novel language-interaction paradigm for energy system simulation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Predict complex system behavior using surrogate models
• Enhance accessibility of surrogates via natural language interfaces
• Improve accuracy and generalization with SysCaps-augmented surrogates

Innovation

• Uses language descriptions for surrogate model interaction
• Integrates multimodal text and timeseries regression model
• Employs LLMs for high-quality caption synthesis