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What is realvirtual.io?

Realvirtual.io provides a Unity-driven platform tailored for the development of industrial digital twins and virtual commissioning in machine and plant engineering. This cutting-edge system empowers users to import their CAD files from multiple formats, including STEP, SolidWorks, CATIA, and NX, while allowing the specification of kinematics such as drives, sensors, and grippers without altering the original geometry of the model. Additionally, it effortlessly incorporates real or simulated PLCs and robot controllers into a unified 3D model through more than 15 native interfaces, which encompass Siemens S7, Beckhoff TwinCAT/ADS, OPC UA, MQTT, EtherNet/IP, Fanuc, KUKA, and ABB. The platform also offers complete C# source code, addressing concerns regarding black box operations and vendor lock-in, while achieving impressive visuals at 60 fps and significantly lowering costs compared to conventional digital factory solutions. A single model can fluidly progress through various phases, including sales, engineering, virtual commissioning, 3D HMI, and operator training. For users seeking to explore the platform, a free Starter edition is accessible via the Unity Asset Store, and the companion product, realvirtual WEB, which is open source and leverages Three.js, facilitates browser access to the same model with a single link, enhancing both accessibility and collaboration opportunities. This combination of features positions realvirtual.io as a versatile tool for industrial applications, making it an attractive option for companies aiming to innovate their operational processes.

What is Unlearn?

Advancing artificial intelligence to eliminate the necessity for trial and error in healthcare, our digital twins enable rapid and reliable clinical trials. We prioritize research in fields such as neuroscience, immunology, and metabolic disorders, among others. TwinRCTs accelerate full enrollment by requiring fewer participants to achieve the same statistical power as traditional trial methods. This innovative strategy drastically cuts down on the time needed for late-stage study enrollment. Furthermore, TwinRCTs improve the detection of treatment effects in early-stage research by increasing statistical power without needing more participants. They allow researchers to make well-informed decisions based on initial findings and assist in attracting additional participants to clinical trials. By utilizing smaller control groups, TwinRCTs also enhance the likelihood of participants receiving the experimental treatment. Our dedication to ensuring that clinical trials with digital twins achieve regulatory approval remains steadfast. Unlearn is leading the charge in revolutionizing the healthcare industry through the pioneering use of artificial intelligence, crafting and applying cutting-edge generative models that utilize extensive datasets gathered from prior patient studies. This shift in methodology not only optimizes research efficiency but also significantly improves the effectiveness of clinical trials. The future of medicine is being reshaped as we continue to refine these innovative approaches.

Media

No images available

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

$0
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

realvirtual GmbH

Date Founded

2000

Company Location

Germany

Company Website

realvirtual.io

Company Facts

Organization Name

Unlearn

Company Location

United States

Company Website

www.unlearn.ai/

Categories and Features

Digital Twin

Not specified

Categories and Features

Digital Twin

Not specified

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