{"id":2899,"date":"2020-01-21T06:29:20","date_gmt":"2020-01-21T11:29:20","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/?p=2899"},"modified":"2026-03-26T12:02:17","modified_gmt":"2026-03-26T16:02:17","slug":"the-opportunities-and-limitations-facing-ai-and-the-digital-twin","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/the-opportunities-and-limitations-facing-ai-and-the-digital-twin\/","title":{"rendered":"Today meets tomorrow: The opportunities and limitations facing AI and the digital twin (Part I)"},"content":{"rendered":"\n

Artificial intelligence (AI) is at the forefront of many industry leaders\u2019 minds as it’s positioned to be a major enabler and disrupter. However, its implementation is still in the early stages. How will designers and engineers of sophisticated, complex systems use artificial intelligence and how will the digital twin help advance its capabilities?<\/p>\n\n\n\n

First, it\u2019s worth noting how AI and the digital twin can work together today to aid designers.<\/p>\n\n\n\n

AI Assisted Design<\/strong><\/h3>\n\n\n\n

AI and machine learning (ML) can assist to create user interfaces based\non expected tasks to\nincrease user adoption and productivity.<\/a> By changing design parameters and running\nAI algorithms, designers can evaluate possible best fits based on the results\nof the algorithms. They can also use data from older, existing designs to uncover\nlatent design properties and then use these findings to improve the current model.<\/p>\n\n\n\n

Generative design is an approach to find an optimal solution\nby systematic variation of parameters, structure or shape of a design. The\nevaluation is mostly done by simulations orchestrated by space exploration\ntools. AI-driven behavior models for well-known parts of the system, or parts\nthat cannot be modeled mathematically but learned instead, speed up these\nevaluations.<\/p>\n\n\n\n

AI can recommend next steps based on predictions of the previously completed design. This supports better design quality by guiding the designer or engineer to the next logical step without having to search for the step in the menu structure.  <\/p>\n\n\n\n

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NX software capabilities leveraging machine learning and artificial intelligence technology  <\/figcaption><\/figure><\/div>\n\n\n\n

Challenges of simulation\nand the digital twin<\/strong><\/h3>\n\n\n\n

Because a digital\ntwin<\/a> is a completely virtual representation of a physical product or\nprocess, speed and accuracy are key when it comes to creating it. The digital\ntwin needs to represent all aspects of the system, ranging from CAD and\nsoftware model simulation to flow dynamics and electrical circuit simulations.\nThere are a number of engineering disciplines that come into play in\ndevelopment digital twins and the variety of model types required to create\nthese virtual representations.<\/p>\n\n\n\n

Today, the ability to combine the models from all the\nauthoring tools in the digital twin flow is limited by the lack of standards.\nEven when there are standards, they do not completely specify the semantics\nrequired for these tools to all behave correctly when combined. <\/p>\n\n\n\n

Engineers and designers can benefit from AI technologies to\nbuild digital twin models. They can compare \u201clearned\u201d behavior against\nsimulation models. This helps in a myriad of ways including:<\/p>\n\n\n\n