Turn Engineering Knowledge
into Operational Intelligence
The Ansys Digital Twin collection helps organizations create connected virtual representations of real assets and systems, combining simulation, reduced-order models and operational data to improve performance across the lifecycle. This supports better decisions across the lifecycle, from predictive maintenance and system optimization to long-term asset performance.
By linking engineering models with real-world behavior, digital twins extend simulation value beyond design and into operation. This helps teams move from virtual validation to smarter, more connected asset intelligence.
Relevant Ansys
SOFTWARE
Ansys Twin Builder
A simulation-based digital twin platform for building, validating, and deploying connected virtual replicas of real assets. Best for organizations using physics-based twins for predictive maintenance, performance optimization, and lifecycle asset management.
Create hybrid digital twins using multidomain system simulation and reduced-order models.
Support model-in-the-loop and software-in-the-loop workflows during development and validation.
Connect digital twins to IIoT and operational data for real-world deployment.
Deploy and scale twins across cloud and industrial platforms more efficiently.
Ansys TwinAI
An AI-powered digital twin solution that combines physics-based models with operational data using AI and machine learning. Ideal for teams that want faster, more adaptive twins with stronger calibration and real-world prediction quality.
Blend physics accuracy with data-driven insights through hybrid analytics.
Use reduced-order modeling and AI/ML techniques to accelerate twin performance.
Support FMI/FMU Composer workflows for more flexible model integration.
Deploy digital twins on cloud or edge-ready IIoT platforms at scale.
From Physics-Based Twins to
AI-Enhanced Digital Twins
One of the biggest strengths of the Digital Twin collection is that it supports both physics-based and hybrid twin strategies. Ansys Twin Builder is used to build, validate, and deploy simulation-based digital twins, while Ansys TwinAI adds AI and machine learning to improve calibration, prediction, and deployment flexibility.
This makes the collection valuable for organizations that need more than monitoring dashboards alone. It provides a practical way to combine simulation accuracy with field data for smarter, scalable digital operations across connected assets and systems.
Relevant
Applications
Predictive Maintenance
Anticipate maintenance needs earlier with connected digital twins that combine virtual models and operational data. Reduce unplanned downtime and improve service planning across complex assets and systems.
Asset Performance Optimization
Use digital twins to improve how assets perform in operation, not just how they were originally designed. Support better efficiency, throughput, and day-to-day operating decisions with physics-based insight.
Lifecycle Asset Management
Manage assets across more of their lifecycle with digital twins that stay connected to real-world behavior over time. Support stronger engineering, operations, and maintenance decisions with a lifecycle-focused digital thread.
Hybrid Physics-and-Data Modeling
Combine physics-based models with operational data to build faster, more adaptive, and more accurate digital twins. Improve prediction quality where physics alone or data alone is not enough.
Reduced-Order Model Deployment
Deploy high-fidelity engineering insight more efficiently through reduced-order models built for operational use. Enable faster digital twin execution where full simulation would be too computationally heavy.
IIoT-Connected Twins
Connect digital twins to IIoT platforms and live data sources to support monitoring, validation, and ongoing refinement. Bring operational data into twin workflows more effectively across industrial environments.
Cloud and Edge Deployment
Deploy digital twins across cloud, edge, or offline environments to support scalable operational rollout. Extend digital twin value beyond engineering desktops into real deployment settings.
Control Strategy Validation
Validate embedded controls with model-in-the-loop and software-in-the-loop workflows linked to physical system models. Improve control performance earlier by testing logic against a virtual representation of the real system.
Connect Design Insight to
Real-World Performance
Without a digital twin strategy, valuable engineering insight often stays locked in the design phase while operations rely on slower, less connected decision-making.
Fluid Codes helps teams apply the right Ansys Digital Twin technologies to bring simulation, operational data, and predictive intelligence together in a more practical workflow.
