Length: 2 Days
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Fundamentals of Digital Twins

Fundamentals of Digital Twins is a 2-day course where participants learn key concepts behind digital engineering and digital twins.

Participants also learn much more such as advantages and benefits of digital twins, digital twin use cases and applications and virtual representation of an object or system that spans its lifecycle.

Digital Twin

Digital twins technology is now paying off for many businesses as they are witnessing substantial ROI on their digital twin initiative.

Other organizations are close behind in turning a profit through their digital twins investments.

Needless to say, there is considerable traction and excitement about what digital twin technology is capable of doing now and in the future.

One of those much ballyhooed trends is the advent of cognitive digital twins.

With the ability to learn at run-time, digital twins are evolving to become cognitive digital twins with artificial intelligence (AI) functions and cognitive capabilities.

Consequently, organizations can now use new applications for digital twins that have been infeasible in the past. Cognitive digital twins are beneficial because they are self-learning and can foresee the future.

A hybrid digital twin integrates data from various sources like sensors and databases with digital twin models. It is an extension of a hybrid digital twin that incorporates cognitive features to enable the sensing of complex reasoning about dynamic process optimization strategies.

Cognitive digital twins that are self-learning may foresee the future and make decisions based on the models, actual data (both batch and stream), and estimated data.

digital-twinsIn cases of lack of data, synthetic data are created based on high-fidelity models, reflecting the actual system.

Generative AI can enhance the concept by providing real-time data for optimization and further analysis. This can bridge the gaps in design, build, and implementation. Additionally, using generative AI can improve the data

quality. This can be particularly helpful where accurate data is scarce.

Generative AI models can be trained on historical data and fault patterns to predict potential equipment failures.

Additionally, implementing AR/VR into digital twins can take the digital twin concept to the next level. Augmented reality can connect the dots between digital and physical assets, making it easier to understand and visualize.

Fundamentals of Digital Twins Course by Tonex

Fundamentals of Digital Twins covers the key principles of Digital Twins and how it relates to integration of digital engineering, modeling and simulations, AI/ML, 3D and integration for service and product-related data and systems. The concept of digital twins is a response to the increasing digitalization of service and product development, production, and digital products worldwide.

Digital twins are virtual replicas of physical systems, devices, services, assets, or processes used to run real time simulations. Digital twins are designed to analyze events, if scenarios, detect and prevent operational and production issues, predict performance, and optimize processes through real-time analytics to deliver and optimize business value.

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

digital twin

Program Outline

Digital Twin 101

  • Key concepts behind digital engineering and digital twin
  • History of digital twin technology
  • How does a digital twin work?
  • Virtual representation of an object or system that spans its lifecycle
  • Digital twin use cases and applications
  • Advantages and benefits of digital twins
  • Key enabling technologies
  • Systems engineering and System of Systems Engineering (SosE)
  • Key concepts behind Model-based Systems Engineering (MBSE) and digital engineering
  • Modeling and simulation 101
  • Overview of UML, SysML, DoDAF and UAF
  • Data science and data analytics 101
  • AI/ML 101
  • Digital twins vs. simulations
  • Integration of 5G, VR/AR and 3D printing

Types of Digital Twins

  • Capability twin
  • System of Systems (SoS) twin
  • System or Unit twins
  • Subsystem twins
  • Component twins/Parts twins
  • Asset twins
  • Process twins
  • Performance measures
  • Master, the shadow and the twin
  • Related digital twin solutions

Digital Twin Market and Industries

  • Defense
  • Aerospace
  • Space
  • Engineering (systems)
  • Automobile manufacturing
  • Aircraft production
  • Railcar design
  • Building construction
  • Manufacturing
  • Power utilities
  • The future of digital twin

Practical Applications

  • Case studies
  • Workshops
  • Build your own digital twin using Tonex framework

Fundamentals of Digital Twins

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