Artificial Intelligence and Neural Networks

Simulation is a key enabler for the growth of AI. Simulation models and Digital Twins utilize AI and machine learning to automate data gathering, aggregation, and analytics to implement Industry 4.0 business models at the resource, process, and facility level.

Simio empowers enterprises to take advantage of AI within simulation models and Digital Twins. You can generate realistic, clean, labeled data that capture the complexities of your enterprise systems with Simio models. Simio Digital Twins provide embedded support for neural networks. You can create thousands of scenarios to evaluate operational outcomes near real-time and to train more robust AI models.

Simio enables stakeholders in manufacturing, aerospace and defense, warehousing, transportation, healthcare, and the Oil and Gas industry to automate workflows and enhance decision making with AI.

Enhancing Decision-Making with Neural Networks

Neural networks reflect the behavioral patterns of the human brain by mimicking the process biological neurons communicate during neural activities. Neural networks consist of node layers including – an input layer, singular or multiple hidden layers, and an output layer. Here, nodes represent artificial neurons and each node has its associated weights and thresholds.

Simio is the first discrete event simulation software to have embedded support for neural networks, which not only includes the ability to use neural networks for inference in the model logic, but also the capturing of training data and an interface for training the neural network model. The Simio implementations of neural networks are feedforward, where data moves from the input neurons of the network to the output neurons and does not loop back to the input neurons.

Simio’s neural network features expand the application of simulation Digital Twins, further supporting the prescriptive aspects of the models and optimizing the design and operation of complex systems. Take advantage of Simio’s neural network features to:

  • Create feedforward neural networks without coding to build models in less time.
  • Improve decision-making to optimize production cycles, improve resource management, and capacity planning.
  • Implement predictive analytics measures to reduce downtime and its effects.
  • Import trained neural network ONNX files into Simio to improve the intelligence of models

Generate Data to Train Your Neural Networks

AI algorithms require a lot of training data – usually more than what can be provided with historical data.  Simio models can generate realistic, clean, labeled data and capture your system’s complexities. Training your neural network using sample data sets increases its decision-making and problem-solving capacity.

You can train your neural networks to learn non-linear patterns so that it makes good predictions when given new data. Simio built-in training feature enables you to generate the quality big data sets required to train neural network models and use them in Simio Digital Twins. The advantages of generating and training neural networks with Simio include:

  • Creating thousands of scenarios to generate big data sets for training AI models
  • Pre-training neural networks for use across diverse applications
  • Normalizing data to speed up the training of neural networks
  • Support for third-party data aggregation –ERPs, MES – platforms to collect training data

Evaluate Your AI/ML Effectiveness

A Simio Digital Twin is connected directly to your enterprise systems and creates a virtual replica of your real system. Simio Digital Twins are the ideal digital environments for testing the effectiveness of your enterprise’s AI algorithms and policies. The virtual replica allows for the creation of different scenarios and situations that the real system might encounter.

These scenarios can be evaluated to test the behavior and outputs of your AI/ML algorithm. The advantages of using Simio to evaluate your AI/ML algorithms include:

  • A safe virtual environment for testing AI frameworks before implementation
  • End-to-end integration for data and AI from your enterprise systems
  • Custom tooling to test and evaluate AI/ML policies without coding
  • Infuse diverse scenarios and situations to test output accuracy