BIMScaler Blog – In recent years, AI digital twins have started to transform various industries. The combination of AI and digital twins is opening up new possibilities for innovation across industries.
From making manufacturing processes more efficient to transforming healthcare, these intelligent twins are set to change our world.
So, let’s take a deeper look at the different elements of these technologies.
What are Digital Twins?
Digital twins are like virtual copies of physical systems. They show how those systems behave, what state they’re in, and how well they perform.
The digital twin technology uses data from physical assets in real time to simulate and predict outcomes in a virtual environment.
Lina Bariah and Merouane Debbah say in “The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches,” how a digital twin isn’t just a data stream from the physical entity.
The digital twin is also a two-way feedback loop, so you can make active adjustments to the physical system based on insights from the virtual model.
In Australia, digital twins are becoming more and more popular in industries like infrastructure development and energy.
They can help with predictive maintenance and make sure resources are used as efficiently as possible, for example in smart building designs.
The best thing about digital twins is that they let you test, model, and analyse complex systems without the risks or costs of manipulating physical objects.
Learn more: “How do Digital Twins Enhance Predictive Maintenance: Beginner Approach.”
How AI Enhances Digital Twin Technology
Artificial intelligence has made digital twin technology much more powerful by automating data analysis and improving prediction capabilities.
Pushkar P. Apte and Costas J. Spanos show how AI can make digital twins more accurate in their paper, “How Human-Informed AI Leads to More Accurate Digital Twins.”
They talk about a hybrid approach – combining human expertise with AI – which improves the precision of digital twins.
This kind of approach is useful in industries where there isn’t a lot of data available, like in the Australian construction sector.
A digital twin of heavy equipment can simulate excavation in unknown terrains, preventing costly damage and ensuring safety.
AI takes digital twins to the next level by letting them do more than just replicate data.
Machine learning algorithms let twins predict what’ll happen, adapt to changing conditions, and optimise operations on the fly.
Apte and Spanos’ research shows how AI, when used with digital twins, can cut energy use in smart buildings by 25-50%. That’s a big plus in sectors like construction and urban planning.
This is especially relevant in Australia, where the construction industry is responsible for a large amount of national energy consumption and carbon emissions.
What’s more, as AI keeps on developing, it’s also becoming more and more integrated into digital twin technology.
For instance, generative adversarial networks (GANs) can create synthetic data, which helps digital twins work well even in places where there isn’t much data.
This is helpful in new market areas, like Australia’s growing clean energy sector, where there isn’t a lot of data on new technologies like hydrogen production.
AI can be used to model these technologies, which speeds up the research and development process.
Components of AI Digital Twins
The AI digital twins are made up of a few key parts that work together to create a really powerful tool for simulation, analysis, and decision-making.
The starting point is a detailed model of the physical entity or process, often created using physics-based simulations or other specialist knowledge.
This model is then hooked up to the physical twin through a network of sensors and actuators, which allows for a two-way flow of data.
The AI part, which usually includes machine learning algorithms, processes the data to learn, predict, and optimise.
In their paper “Artificial intelligence in digital twins – A systematic literature review,” Tim Kreuzer and colleagues show that the most common tasks AI performs in digital twins are optimization (32.9%), classification (31.6%), and regression (21.9%).
Plus, a user interface lets human operators interact with the digital twin, see data, and make informed decisions based on the AI’s insights.
The combination of these elements creates a flexible and responsive system that can provide valuable insights and drive innovation across a wide range of applications.
Benefits of AI Digital Twins
Bringing AI and digital twins together is a great move. It’s a win-win for a lot of different industries. As Kreuzer and others have shown, AI digital twins can be used to predict things.
For example, we could use it to forecast when equipment might fail or to anticipate when a patient’s health might deteriorate.
This means we can take action before things get out of hand and make sure we’re using our resources in the best way possible.
Being able to try out different scenarios in a safe virtual space helps us to be more creative, cut development costs, and get products to market faster.
AI digital twins are great for optimisation too. They learn from data to make processes more efficient and improve overall performance.
The Kreuzer et al., review found the most common task AI in digital twins was optimisation, which appeared in 32.9% of the analysed studies.
Also, AI digital twins can adapt and evolve along with their physical counterparts, so their models stay accurate and relevant even as real-world systems change.
This ability to adapt is particularly valuable in complex and unpredictable environments, where traditional models may struggle to keep up.
Learn more: “What are the Key Benefits of Using Digital Twins in Manufacturing?“
Future Trends in AI Digital Twins
Edge computing is going to let us process data in real time, make decisions straight away, and reduce latency.
This approach is going to make AI digital twins more responsive.
The rise of 5G and beyond will make this trend even faster-paced, giving us the high-bandwidth, low-latency connectivity we need to support the huge amounts of data generated by AI digital twins.
As Bariah and Debbah talk about in their paper, the combination of AI and digital twins is set to be a key part of making 6G networks a reality.
And it will help us manage networks in smarter, more autonomous ways.
Also, as AI gets smarter, digital twins will be able to do more complex tasks, like predicting new behaviours or optimising large-scale systems.
It’ll also be important to incorporate explainable AI, so we can see exactly why AI digital twins make certain decisions and build trust in them.
How to Implement AI Digital Twins
The first step is to build a detailed model of your physical system, whether it’s a factory floor, a wind turbine, or even a smart city block.
And then comes the fun part: the connection.
You’ll link this digital twin to your real system using a network of sensors and actuators, creating a feedback loop that gives the model some real-world context.
The data just keeps on flowing, like a steady drip of coffee in the morning.
The AI is in charge, crunching numbers, learning patterns, predicting breakdowns, and optimising performance as if it’s second nature.
No guesswork is involved. It’s just pure, data-driven muscle.
And don’t worry, you don’t need to be a machine learning expert to interact with it.
A simple, easy-to-use interface will let you see the data, adjust settings, and make decisions in the moment with ease.
Now, here’s where it gets interesting. Building Information Modelling (BIM) isn’t just for architects anymore.
It’s the foundation of AI digital twin implementation, providing the framework to manage all that data and make processes more efficient.
And that’s why we at BIM Scaler are not just blowing smoke.
We’re talking Revit modelling, digital engineering, model auditing – the whole nine yards.
We make sure your digital twins are always in sync with the real-world versions, stopping data corruption and making sure everything goes smoothly from design to operation.
We’ll even throw in 4D and 5D planning for good measure, so you’ve got advanced scheduling and cost control throughout your project’s lifecycle.
Ready to see how this all works in the real world? For all the details, kindly visit our BIM Management Support page.
Or better yet, let’s grab lunch.
We’ll talk shop, share a few laughs, and figure out how to turn those complex digital dreams into reality, one step at a time.
In Closing
From predictive maintenance and resource optimisation to autonomous systems and smart cities, there are so many ways AI digital twins can be used.
As we keep pushing the boundaries of these fascinating AI digital twins, we can look forward to even more groundbreaking developments that will shape the future of technology and redefine what’s possible.
