Generative Transformer Digital Twin Generation
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Solution Overview
Problem
Existing digital model and digital twin generation technologies lack the ability to accurately represent real-world objects in virtual environments and simulate real-world conditions effectively, leading to less accurate and realistic simulations.
Innovation Solution
The use of generative transformer networks and large language models to generate digital twins of physical systems, allowing for the creation of accurate digital representations of real-world objects and environments, and enabling simulations that closely mimic real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional CAD tools and virtual reality tools are used to create digital models, then the design process can be virtualized and iterated computationally, but the models lack accurate representation of real-world conditions and require manual code writing
Solution Approach 1:
The patent uses generative transformer networks to automatically generate digital twin code that copies and represents physical system behavior. The LLM learns from training data to create accurate virtual representations of real-world systems, enabling the digital model to replicate real-world conditions without manual programming of each detail.
Solution Approach 2:
The patent replaces manual code writing and traditional modeling approaches with AI-based generative models. The trained parameterized model automatically generates the code structure and parameters for digital twins, substituting the mechanical process of manual programming with intelligent automated generation.
2Adaptability or versatility
If manual code writing is used to describe model parameters in virtual environments, then flexibility and customization are achieved, but time consumption and complexity increase
Solution Approach 1:
The patent performs preliminary action by training the parameterized model on comprehensive training data that includes diverse physical system characteristics and behaviors. This pre-training enables the model to quickly generate customized digital twin code without requiring manual programming for each new system, reducing development time while maintaining adaptability.
Solution Approach 2:
The generative transformer network performs self-service by automatically generating the code structure, parameters, and logic for digital twins based on the physical system description. The system serves itself by using the trained model to produce the necessary code without human intervention in the actual code writing process.
3Measurement precision
If digital twins are generated without trained parameterized models, then simpler systems can be used, but accuracy and realism of simulations decrease
Solution Approach 1:
The patent applies preliminary action by training the parameterized model in advance on extensive training data that captures real-world system behaviors and characteristics. This pre-training investment creates a knowledge base that the model uses to generate accurate digital twins, achieving high simulation accuracy without requiring complex manual modeling for each case.
Data Source
AI summary
Digital model and digital twin generation using generative transformer networks and large language models is described. A digital twin generator is configured to generate a digital twin of a physical system useable for electronic testing with simulated real world conditions in a digital environment. Real world objects are represented accurately in a virtual world digital environment. The digital environment can be updated based on real world conditions. The present systems and methods are configured to enable more accurate and realistic simulation with the real world objects and/or physical systems in the real world conditions compared to prior systems.