Computing and production giants have realized that in order to predict outcomes in large-scale systems truly you need more than just a simulation. There are many definitions of a digital twin but the general consensus is around this definition "a virtual representation of an object or system that spans its lifecycle, is updated from real-time data and uses simulation, machine learning, and reasoning to help decision-making."
By having better and constantly updated data related to a wide range of areas, combined with the added computing power that accompanies a virtual environment, digital twins are able to give a clearer picture and address more issues from far more vantage points than a standard simulation can, with greater ultimate potential to improve products and processes. Here are some thought-provoking examples of how "Twinning" is used to manage and predict production by creating mathematically deterministic models that are aware of changes and rules so they produce better analysis, prediction and production.
NVIDIA developed a platform for scientific digital twins that accelerates physics machine-learning models to solve million-x scale science and engineering problems thousands of times faster than previously possible.
The platform can create interactive AI simulations in real time that are physics-informed to accurately reflect the real world, accelerating simulations such as computational fluid dynamics up to 10,000x faster than traditional methods for engineering simulation and design optimization workflows. It enables researchers to model complex systems, such as extreme weather events, with higher speed and accuracy when compared to previous AI models.
This EU consortium develops personalized hybrid brain models uniting the physics of electromagnetism with physiology, neuro-twins or NeTs develop advanced brain models that characterize individual pathology and predict the physiological effects of trans-cranial electromagnetic stimulation, and use them to design optimal brain stimulation protocols in Alzheimer's disease. They are building a computational framework, weaved and validated across scales and levels of detail, to represent the mechanisms of interaction of electric fields with brain networks.
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The Shanghai Urban Operations and Management Center has built a digital twin of the city of 26 million inhabitants, which models 100,000 elements from refuse disposal and collection facilities to e-bike charging infrastructure, road traffic, and the size and location of apartment buildings. Its creator, 51World, uses data from satellites and drones to construct the living model.
Tesla creates a digital simulation of every one of its cars, using data collected from sensors on the vehicles and uploaded to the cloud. These allow the company's AI algorithms to determine where faults and breakdowns are most likely to occur and minimize the need for owners to take their cars to servicing stations for repairs and maintenance. This reduces cost to the company of servicing cars that are under warranty and improves user experience, leading to more satisfied customers and a higher chance of winning repeat business.
The cloud is one of the most complex environments to manage. In order to run it properly you need to understand context and how a specific Infrastructure change will effect your entire system.
Our CloudTwin technology creates an exact copy of your environment in real time so you can simulate the changes you want to make and understand their impact on the entire system.
So what are you waiting for? want a CloudTwin of your own?
Stream.Security delivers the only cloud detection and response solution that SecOps teams can trust. Born in the cloud, Stream’s Cloud Twin solution enables real-time cloud threat and exposure modeling to accelerate response in today’s highly dynamic cloud enterprise environments. By using the Stream Security platform, SecOps teams gain unparalleled visibility and can pinpoint exposures and threats by understanding the past, present, and future of their cloud infrastructure. The AI-assisted platform helps to determine attack paths and blast radius across all elements of the cloud infrastructure to eliminate gaps accelerate MTTR by streamlining investigations, reducing knowledge gaps while maximizing team productivity and limiting burnout.
