Imagine having a virtual copy of yourself that lives inside a computer. This virtual you is an exact replica of real-world you- complex data structures work together to recreate your unique genome in the digital world. This, essentially, is the idea of a Digital Twin. They combine machine learning, real-time analytics, VR, MR, and AR, to produce a virtual representation of something that exists physically in the real world. Digital Twins can be a replica of any ecosystem or product: a human, a building, a spaceship, the list goes on. To create a Digital Twin, sensors in the real-world monitor the object being replicated. Data from these sensors is constantly being fed into the computer model composing the Digital Twin. Put simply, the digital version is mimicking what is happening to the real-world system in real time (TWI Global). This allows us to better understand performance, predict problems, and come up with better solutions ahead of time.
Though the advancement of this technology through the introduction of XR is relatively new, the idea of “Digital Twins” goes back to the 1960s, when NASA began experimenting with more complex simulations for their space exploration missions (IBM). The flight crew for each voyaging spacecraft studied a replica of their spacecraft simulated as an earth-bound version of itself to understand how to prepare for the journey back.
The idea of a Digital Twin may sound like it’s nothing more than a simulation of the real world. While that’s not entirely wrong, Digital Twins are actually so much more than that. Simulations and Digital Twins both provide virtual models replicating a real-world system. However, simulations do not make use of real-time data, whereas Digital Twins do. As developers at IBM put it, they make use of a two-way flow of information “that first occurs when object sensors provide relevant data to the system processor and then happens again when insights created by the processor are shared back with the original source object” (IBM). This enables systems to be studied from so many different perspectives, far more than is possible with a simulation. It also allows us to study these systems with a higher level of specificity and customizability. In turn, we’re able to make more exact predictions and give more exact solutions.
Digital Twins are already being used in a variety of industries, from the more obvious use cases, like manufacturing and automotive, to more recent developments, like in healthcare and climate change management. In manufacturing, Digital Twins are able to help keep processes streamlined and efficient. In automotive design, they can be used to test features and inform product improvements. In healthcare, there is research being done around replicating entire organ systems at the specific individual level (so that a patient can see exactly how they might react to certain medications before taking them). They’re also being used at a higher level to track disease flow and pinpoint danger areas. Climate change is a serious problem that has an opportunity to be mitigated through Digital Twin models. Through monitoring climate change in real-time, we can make highly accurate predictions for the future. These models could inform how we adapt through creating more sustainable infrastructures and how we respond in emergency situations.
As you can imagine, the applications of this technology are nearly endless. As we enter the age of big data, we are able to more clearly and accurately understand our world. Paired with visual modeling using XR, this data has the potential to completely change how we track performance and problem-solve, in all industries.
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