Hybrid analytics is a set of ML tools for combining physics and data differently. By making smarter choices in selecting your training data and ML techniques, you can open up new possibilities for your hybrid digital twin. Thanks to hybrid digital twins, join us for this webinar and learn how to get the most accurate twins.
Venue:
Virtual
Digital Twins are virtual representations of real-world entities and processes, synchronized at a specified frequency and fidelity to track the past, provide deeper insights into the present, and predict and influence future behavior. Ansys Twin Deployer streamlines a Twin Model's validation process in an environment closer found in deployment environments (simulation engine, operating system, and data streams).
A hybrid digital twin is a digital twin that combines both physics and data. In other words, hybrid digital twins don't rely on simulation or machine learning (ML) alone but use both methods to leverage all available knowledge about a system. Engineering informs a physics model, while data provides new insights to inform that model. The techniques and tools available to combine physics and data constitute a hybrid analytics toolset.
Hybrid analytics is a set of ML tools for combining physics and data differently. By making smarter choices in selecting your training data and ML techniques, you can open up new possibilities for your hybrid digital twin. One area where this is most evident is in fusion modeling, i.e., combining at least two different types of data to train an ML model.
Mohammad Aghsaee
