Language-Model Symbiotic Training for Adaptive Digital Twins
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Solution Overview
Problem
Creating a digital twin is a time-consuming and complex process that requires significant data acquisition and processing, and the resulting model may be static, making it difficult to adapt to real-time changes in the environment.
Innovation Solution
Utilizing large language models (LLMs) to simulate the behavior of the original system through symbiotic training, where inputs are fed to both the original system and the digital twin, and outputs are compared to refine the digital twin's accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional digital twin creation methods are used, then the digital twin can represent the original system, but the process is time-consuming and complex requiring significant data acquisition and processing
Solution Approach 1:
The patent uses a language model to generate a digital twin by copying and simulating the behavior patterns of the original system through text-based interactions. Instead of creating a detailed computational model through extensive data acquisition, the system creates a linguistic copy that can respond to prompts in ways that mirror the original system's behavior, significantly reducing creation time while maintaining representational accuracy.
Solution Approach 2:
The patent replaces traditional mechanical data acquisition and model-building processes with a language-based system. Instead of systematically collecting and processing numerical data to build computational models, the system uses natural language prompts and responses to capture system behavior, substituting a linguistic mechanism for a data-intensive mechanical process.
2Adaptability or versatility
If traditional digital twin models are created, then they can simulate system behavior, but they are static and difficult to adapt to real-time changes in the environment
Solution Approach 1:
The patent creates a dynamic digital twin by using a language model that can continuously adapt its responses based on new inputs and interactions. The digital twin evolves through ongoing prompt-response cycles, allowing it to adapt to real-time changes in the environment without requiring complex reconfiguration or retraining of the underlying model structure.
Solution Approach 2:
The patent achieves adaptability through parameter changes in the language model's responses rather than through structural model changes. By adjusting the linguistic parameters and response patterns based on new information and interactions, the digital twin can adapt to changing conditions while maintaining the same underlying model architecture, thus avoiding increased complexity.
3Measurement precision
If extensive data acquisition is performed to create an accurate digital twin, then the model precision improves, but the process becomes more complex and time-consuming
Solution Approach 1:
The patent substitutes a language-based interaction mechanism for complex data acquisition and processing systems. By using natural language prompts to elicit and capture system behavior, the method achieves measurement precision through qualitative linguistic observations rather than through complex quantitative data collection and analysis processes.
Solution Approach 2:
The patent creates a behavioral copy of the system through language interactions rather than through detailed data replication. The language model learns to mimic the system's response patterns and decision-making processes through prompt-response training, achieving behavioral precision without requiring extensive data acquisition infrastructure or complex data processing pipelines.
Data Source
AI summary
Methods and systems for training a digital twin include submitting a hardware prompt to a language model that characterizes hardware of an original system. A software prompt is submitted to the language model that characterizes software of the original system. A discriminant model is trained to distinguish between outputs of the original system and outputs of the language model. The language model is tuned to act as a digital twin of the original system based on an output of the discriminant model, an output of the language model, and an output of the original system.