The Atomic Nature of Digital Twins

6 min readFeb 6, 2023


The fuel that drives the Industrial Metaverse

Just the name “Digital Twin” seems somewhat self-explanatory. With the evolution of Metaverse technology, it is a term that has become increasingly understandable thanks to the immediate visual recognition of a virtual object, product, or environment. A virtual 3D rendition that mimics a real-world counterpart. Visually we can make the virtual twin look the same. We can replicate human interactions that occur in the real world. We can make these twins respond and react as their real-world counterparts do. This opens up a plethora of experiential opportunities. As a consumer we can try before we buy, getting a good sense of what a real-world experience with a product might be. As a creator we can test designs and develop products by distributing virtual twins to anyone, anywhere, ensuring deeper insights, more validated pathways forward, and faster time-to-market. The list of ways we can connect people and products with these technologies is extensive.

Data as a Digital Twin Lifeblood

Dig deeper into digital twin technology and it can quickly move well beyond one-to-one mimicry of real-world physical products. Adding data and developing models that replicate real-world responses means digital twins truly become as much, if not more, a data tool than an experiential and visualization tool. We can now extend our reference to digital twins well beyond the obvious physical products we interact with every day, and step into a world where we twin processes, team dynamics, the physical locations that they operate within, and, ultimately, entire organizations.

Atomic Digital Twinning

Healthtech is an area we’ve seen strong adoption of extended digital twin exploration. By taking the same principles that we would mimic a consumer product the medical world is now creating digital twins of organs and even bodily systems. We can use historical data to train the models that drive these twins. We can then run scenarios and add more data inputs into the models to view and predict responses. A safer and more cost-effective way to undertake research that gets us to positive medical innovations faster. When we look at these medical examples, the atomic nature of digital twins becomes a little more obvious. Human bodies are made up of many systems and individual organs, all working together, but also having their own identity of sorts. We can look at them alone, or we can look at them together. Combinations of twins simply create twins on another level, giving us new insights and new information fast.

Atomic Digital Twins and the Industrial Metaverse

At Younite-AI, we have found in our work in the automotive industry, that the atomic nature of digital twins fuels is an increasingly powerful foundation in industrial settings. One that forms the basis of what is referred to as the “Industrial Metaverse”. In the automotive industry, the physical output of these organizations is vehicles. These are obvious targets to be twinned digitally so we can extend their reach, understand them better, and develop new technologies for them. As we dig into a vehicle we enter a world of systems, sub-systems, and individual parts. Do you now see the atomic structure and the correlation to our medical twins?

From any node within a Digital twin structure, there are further layers of opportunity to develop more granular, or more complex digital twins.

We now have twins within twins within twins, diving deeper and deeper down from our reference point. Beyond just the pieces and their physical representations we now have an atomic twin structure with many nodes to attach data to, and that data can be any or all data associated with those parts. This data also allows us to start building twins outwardly from our vehicle “node” to start creating the twins relating to their manufacturing, and the many processes and specific needs associated with it. It can be financial data coming from the costs of items from suppliers as they fluctuate in fluid markets. Physical location information as vehicles and parts are distributed from plant to plant for assembly. It can be real-time information from the factory floors where the real-world counterparts of our vehicle twins are physically being made and rolled out every single day. We know where those vehicles, along with all their pieces and parts, are in their individual lifecycle within the production process.

We can also weave in data sources external to the organization, such as simple as weather patterns. Even with this readily available data, we can start to see how all of the combined data informs us with deeper and deeper insights. These insights then positively influence the operations of an organization such as predicting weather-related distribution stresses and allow the organization to react and respond proactively, as opposed to reactively. All this information revolving around, and living with, our digital twins creates positive business-impacting possibilities across an entire organization.

A high-level illustration of the data flows through the actionable layers of an Industrial Metaverse resulting in an eco-system that augments its data sources with additional generated data to continually scale and offer perpetual growth in benefits and insights.

By looking just at the data flow, the models, and the outcomes, then contextualizing it around new references to be twinned, the industrial metaverse eco-system still holds true and shows its flexibility. Through our own clients, we see the same structure be relevant in product manufacturing, health and medical products and services, and even farming and agriculture through the entire production lifecycle from seed to consumer.

In Conclusion

The power of Digital twins and the industrial Metaverse is clear, and its benefits are huge. It seems like a massive undertaking, but in reality, it’s one, that we have found at Younite-AI, that can start small, methodically grow, and always deliver business benefits, almost from day one.

Creating digital twins and seeding an industrial Metaverse can start from historical data, using the references of the influence the data had to train models to replicate results. With models replicating responses to the data inputs, we can then manipulate our twin to understand how we could have achieved better outcomes. This allows us to move forward with validated insight.

Because data lives and breaths in real-time, we can use AI tools to crunch this data far faster than any human. We can show its influence on our twin and deliver the results in real-time and visualizations allow us to make real-time informed decisions fast, and make them from anywhere. With AI and ML we can create models that replicate how these twins react to any variety and combinations of data inputs. This gives us foresight and predictive opportunities, allowing us to constantly test and learn by adjusting our twins and understanding, with high degrees of certainty, what the results will be, positive or negative.

What we’ve found at Younite-AI is that even though the industries may be vastly different and the organizations may be entirely unique, the benefits from Industrial Metaverse and Digital Twin technologies are always applicable and always beneficial. You just need to get started.

About the Author:

Dave Papworth is the Creative Cultivator and Product Leader for Younite-AI. His career has taken him through multimedia, development, design, innovation, and ultimately Younite-AI.

While working in the advertising industry he led teams focused on technical innovation and how it can be leveraged for marketing campaigns and brand building platforms, creating forward-thinking projects that have been showcased at events like Googles Sandbox, and even recognized in Time Magazine as an “Invention of the Year”.

At Younite-AI, his focus is on building a team that can tackle any challenge, look beyond their boundaries, and grow the collaborative relationships we desire with our clients.