Real-Time Digital Twin
Virtual twin technology is being adopted by industries worldwide as a powerful way to develop, test, and optimize the design and employment of countless products and services. By adding the real-time element, these real-time digital twins can speed up analysis, forecasting, decision-making, and productivity in a wide range of applications. This development in AI makes it possible to offer on-the-fly adjustments while interacting with people in real time, ultimately resulting in greater efficiency and performance.
What Is a Real-Time Digital Twin?
A digital twin is a virtual model of a physical object or system. It is designed to copy the behavior of an actual object by reacting in a way that simulates what the actual object does.
Take a digital model of a bicycle as a simple example. If a bike doesn’t travel at a certain speed or leans to one side beyond a certain angle, it falls. A digital twin will copy these characteristics as a way, for instance, to test the design of a bicycle race track.
Organizations use digital twins for many different reasons, including:
- Designing products through advanced planning that avoids having to fine-tune physical models
- Building virtual product components to ensure they are compatible before making physical products
- Testing products at ranges that would damage actual products
- Predicting failure times and maintenance needs
While the concept of digital twins only dates back to 2010, it has grown rapidly in complexity through AI. Along with massive increases in processing speed, this development adds a “real-time” dimension to digital twin applications. Real-time allows virtual twins to react in response to data precisely as it is being received. This is in contrast to the forms of digital twins that run programmed simulations using a pre-existing dataset.
Applications of Real-Time Digital Twins
With real-time capability, this technology is finding an expanded set of uses. These include:
- Interactive digital twins in the form of a personal avatar that converse with people for sales, customer service, learning and development, entertainment, and other informational uses
- Control of public utilities such as power grids by receiving real-time data from the system and calculating what, when, and how the system might fail
- Real-time product design that combines simultaneous feedback from actual and virtual products; an example is analyzing the data from a real aircraft in a wind tunnel when the digital twin is subjected to temperature changes
How Do Real-Time Digital Twins Work?
Regardless of application, the essential step in creating any digital twin is to build an exact model of the “real” object’s behavior. Let’s take interactive digital humans as an example. Modern AI-powered digital avatars can be created by taking a video of the person whom you want to copy. The video is then analyzed to design a three-dimensional image of the person’s face and to model how their face moves, mainly when speaking.
The technologies employed in this process include motion capture for essential imaging and AI-powered analysis that samples facial movements and connects them to what the person is saying within very short timeframes. A similar sampling process is used to copy an actual voice.
The next step is to give the model real-time input to compute a reaction. For instance, you might ask the digital human, “How are you doing today?” And then it will:
- Reference a database of potential answers
- Use reinforcement learning to decide on the best response
- Answer your question by turning its textual answer into audio signals, lip-synching, and facial movements that are derived from the modeling process
How Real-Time Digital Twin Technology Is Evolving
Artificial intelligence requires a lot of computing power. To support its continued growth, the world needs much more capability, with some projecting that processing speed will be a million times faster in ten years.
Along with this boost will come an even wider range of AI-based applications and greater use of digital twins that are increasingly similar to their real-life counterparts. Digital humans are expected to look even more like actual people as the rendering process becomes faster. But they will also have the ability to react to and express feelings. This involves leveraging AI as a way to access databases with details about human emotions and expressions that are created by modeling massive amounts of information related to how real people think and feel.
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