Foundation Models for the Digital Twin Creation of Cyber-Physical Systems
arXiv:2407.18779 · doi:10.1007/978-3-031-75390-9_2
Abstract
Foundation models are trained on a large amount of data to learn generic patterns. Consequently, these models can be used and fine-tuned for various purposes. Naturally, studying such models' use in the context of digital twins for cyber-physical systems (CPSs) is a relevant area of investigation. To this end, we provide perspectives on various aspects within the context of developing digital twins for CPSs, where foundation models can be used to increase the efficiency of creating digital twins, improve the effectiveness of the capabilities they provide, and used as specialized fine-tuned foundation models acting as digital twins themselves. We also discuss challenges in using foundation models in a more generic context. We use the case of an autonomous driving system as a representative CPS to give examples. Finally, we provide discussions and open research directions that we believe are valuable for the digital twin community.
References in corpus (13)
- Learning Transferable Visual Models From Natural Language Supervision
- A Survey of Large Language Models
- The Rise and Potential of Large Language Model Based Agents: A Survey
- A Survey of Hallucination in Large Foundation Models
- Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems
- A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends
- Knowledge Distillation-Empowered Digital Twin for Anomaly Detection
- Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
- TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
- Personalized Autonomous Driving with Large Language Models: Field Experiments
- An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems
- Model Generation with LLMs: From Requirements to UML Sequence Diagrams
- HITA: An Architecture for System-level Testing of Healthcare IoT Applications