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Synthetic data and digital twins are very different, but they are complementary in the sense that they simulate AI data and simulate the interaction between humans and AI models.

Synthetic data is artificially created information rather than being generated by actual events or processes. This is algorithmically created data that is used in place of test datasets for production or operational data to validate mathematical models and more often train machine learning models these days.

To generate synthetic data, use the original dataset and train the joint probability distribution using a generative model that samples new data.

Digital twin software is an advanced form of simulation software that uses sensor data from embedded machines to create accurate real-time simulations of assets. These virtual models enable intelligent structural evaluation and analysis, providing actionable insights into machine performance and maintenance needs.

Digital twins perform what-if-scenario analysis by providing an interface that replicates and responds to human and environmental inputs as if the physical twins were acting on the physical items they represent. Make it possible. With digital twins, you can run multiple experiments and scenarios to achieve defined parameter and output optimizations without taking the equipment or processes represented by the digital twins offline.