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Neural Networks and the Digital Twin

Digital twins are virtual representations of physical entities including objects, facilities, and processes. The cyber-physical space that compromises of the digital twin and what it represents facilitates the real-time exchange of data which provides a platform that supports diverse use cases.

Use cases include the application of predictive and prescriptive analysis to discover future challenges and gain optimized solutions to mitigate them. In industrial sectors such as manufacturing, the digital twin is also applied as a remote monitoring tool, capacity planning and real-time scheduling digital transformation solution.

Improving decision making and optimizing workflows is where neural networks are required. Artificial neural networks (ANN) are leveraged to develop complex digital twin models and solve regression problems within the manufacturing floor. Utilizing neural networks improves the decision-making capacity of the digital twin through the continuous training of the digital twin to respond to new challenges in real-time and optimize everyday operations.

 - Success in Simulation and Scheduling

Enhancing Decision Making with Neural Networks in Simio

Simio is the first discrete event simulation and Digital Twin Software to have embedded support for the use of neural networks to design and train models to enhance industrial operations, analyze complex challenges, and for decision-making. Simio provides features that allow users to leverage neural networks in diverse ways including:

  • The creation of feedforward neural networks in Simio without the need for coding algorithms that simplify the integration of AI within digital twins.
  • A built-in trainer that supports the continuous supervised and unsupervised** training of neural networks to improve the accuracy of the network.
  • The choice of importing ONNX files of trained neural networks from other AI networks or platforms to Simio.

Leveraging neural networks within Simio helps with automating workflows associated with improving decision-making, scheduling, and planning within industrial facilities.

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