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Ansys for Hybrid Digital Twins

Combining physics and data modeling to enable real-time monitoring, predictive maintenance, and performance optimization. 

Hybrid Analytics

The Best of Machine Learning and Physics

A hybrid digital twin is a virtual representation of a connected physical asset made possible by combining advanced simulation and analytics. 

Used across various industries, from virtual sensoring to learning unmodeled physics, hybrid digital twins help companies solve many challenges and enable real-time monitoring, predictive maintenance, and performance optimization of systems and processes.

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    Real-time monitoring
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    Predictive maintenance
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    Systems performance optimization of systems
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    Systems performance of processes

Bridge Between Design and Operations Process


Reusing engineering design in operations

A hybrid digital twin originates during design, utilizing design models to capture intent and specs. Reduced Order Modeling (ROM) technology enables complex CFD and FEA models for real-time applications, broadening possibilities for complex systems.

Look-ahead performance

Monitoring assets in the field is valuable, but predicting future behavior is ultimately where digital twins provide maximum value. Hybrid digital twins are critical for look-ahead performance prediction in unbounded conditions.

Designing for data and learning from data

A hybrid digital twin embodies a synergy between data and simulation where the simulation provides more data for data models. In contrast, sensor data from the field teaches the model about operational performance.

Closing the design loop

By continuously learning from field data, an updatable twin shows which components and parameters change over time. This information can predict the remaining useful life of a piece of equipment. Tracking this is valuable for predictive maintenance and design teams to see whether equipment performs as expected.

Featured Resources


BY: Matt Adams

Hybrid Analytics: A Tool Set for Building Hybrid Digital Twins

By making smarter choices in selecting your training data and ML (Machine Learning) techniques, you can open new possibilities for your hybrid digital twin. 

BY: Graziella Alves

How Simulation Enables Continuous Manufacturing

Once a digital twin is deployed, users can expect a 25% increase in product performance and maintenance cost savings up to 20% over the product’s lifetime.

BY: Matt Adams & Vitor Lopes

Get the Best Digital Twin with Hybrid Analytics

Hybrid digital twins combine the power of physics and data modeling to enable real-time monitoring, predictive maintenance, and performance optimization.

BY: Asmaa Lapouge

Enhancing Operational Efficiency with Hybrid Digital Twins: An Interview with Vitor Lopes

In this interview with Vitor Lopes, senior product sales manager at Ansys, we will get a deeper understanding of hybrid digital twins and the typical use cases for this technology in the energy industry.



Get the Best Digital Twin with Hybrid Analytics

Create Digital Twins that behave as closely as possible to physical systems and equipment.


How to Get the Most Accurate Twins with Hybrid Digital Twins

Hybrid Digital Twins ingest field data to adapt to changing environmental and operational conditions.


See What Ansys Can Do For You

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