Physical entity diagnostic system using digital twins and large language models

An apparatus, comprising:

• at least one processing circuitry;
• and
• a memory coupled to the at least one processing circuitry, the memory comprising instructions that, when executed by the at least one processing circuitry, cause the at least one processing circuitry to:
• train at least one large language model (LLM) to predict a component failure of a physical entity based on a digital twin of the physical entity, the digital twin virtually representing the physical entity and a plurality of components of the physical entity, and simulation scenarios based on a simulation objective and execution of the simulations for the simulation scenarios,
• wherein the simulation scenarios comprise off-line test simulation scenarios and online real-time simulation scenarios.

A vehicle, comprising:

• a plurality of components;
• a plurality of sensors configured to measure information of the plurality of components;
• and
• an on-board computer system comprising a memory coupled to a processing circuitry, the memory comprising instructions that, when executed by the processing circuitry, cause the at least one processing circuitry to:
• access a digital twin of the vehicle, the digital twin virtually representing the vehicle and the plurality of components of the vehicle, the one or more components comprising at least one of a transmission component, a braking system component, a steering component, or an exhaust system component,
• access sensor data of the plurality of sensors to reproduce operating conditions of the vehicle via the digital twin, and
• execute a plurality of large language model (LLM) agents trained to predict a component failure of one of the components of the physical entity virtually represented by the digital twin,
• wherein:
• at least a first one of the plurality of LLM agents is trained to predict a failure of a specific component of the plurality of components, and
• at least a second one of the plurality of LLM agents is trained to receive tokenized non-text data from at least one of the plurality of sensors and to generate text-based output.