Digital Twin algorithms in the context of the Industrial Metaverse have numerous applications including:

• Simulate the entire power grid to power a healthy planet
  Create a sustainable, resilient, and energy conscious world by simulating the complexities of our power grid in real-time - the largest man-made machine on the planet. 

• Simulate complex industries to power true sustainability
  Create regenerative factory ecosystems across the world to dynamically simulate production based on environmental-driven-design and minimizing negative carbon footprints.

• Simulate the human body to power healthy societies
  Create the best health outcomes for all by combining our intelligence with artificial intelligence to simulate and predict likely root causes of disease without our human bodies.  

• Simulate the meta world to power the real world
  Create the most advanced asset model representations with real world physics to design-build-and-test everything from transportation networks, airplanes, and spacecraft as quickly as human ideas take flight.  

Today if someone creates a Digital Twin model of a pump, compressor, gas turbine, transformer, motor, drive, pipeline, or any industrial asset, that asset model typically is used in the context of one use case, in one application, and in general - the value-stream starts and stops here.  Digital Twins are use case specific, application specific, industry specific, company specific, and are generally siloed by a lack of transferability.  

There are many open-ended questions today that act are barriers to progress: 

• How do you build reusable Digital Twins to maximize composability? 

• How can you share Digital Twins analytical models in an ecosystem? 

• How do you ensure the performance of a Digital Twin from a 3rd party? 

• How can you retain Intellectual Property ownership of a Digital Twin?

Meta Asset Data Challenges: Data Management, Connectivity, Integrations, Platforms, Architecture, etc.

Types of Digital Twin Algorithms

Digital Twin algorithms can be both very simple, and very complex - encompassing a very wide range of intricacy.  A Digital Twin algorithm can also fall under various classifications which too affect the nature of its construction: not all Digital Twins are created equal.  Two broad categories of digital twins emerge - and fundamentally, even within these two categories, the degree of complexity can vary widely. 

Data-Driven Digital Twin examples:

• Data-Centric Digital Twin 

• Pattern-Based Digital Twin 

• Regression-Based Digital Twin 

• AI/ML Digital Twin 

• Edge-Based Digital Twin 

Use Cases:  Backward Pattern-Finding & Monitoring Current Condition

Physics-Based Digital Twin examples:

• Physics Informed AI Digital Twin 

• Modeling-Based Digital Twin 

• Computer Aided Design (CAD)-Based Digital Twin

• 1D, 2D, 3D Digital Twin 

• Reduced Order Model (ROM)-Based Digital Twin 

Use Cases: Making Forward Predictions 

Instead of attempting to define exactly what a Digital Twin algorithm does in precise terms around languages, models, applications, platforms, and data interchanges - it is better to note that despite how a Digital Twin is defined, the representation of that algorithm can be defined in a root file or files that detail the interactions between (1) Inputs, (2) Languages, (3) Models, (4) Applications & Platforms, (5) Use Cases, and (6) Outputs.  

References