A simulation engine that lets you test reality—before reality tests you
Use a high-fidelity simulation engine built into intelligent digital twins to explore what-if scenarios, understand system behavior, and choose actions with confidence—without disrupting live operations.
Definition
What is a simulation engine in an intelligent digital twin platform?
A simulation engine executes dynamic models of real systems to predict how assets, processes, and networks will behave under different conditions. When embedded in an intelligent digital twin, simulation runs on live, contextualized data—allowing teams to test decisions, explore scenarios, and evaluate trade-offs safely before acting in the real world.
Why offline and static models don't scale
Many organizations already simulate—but still struggle to trust the results. Common issues include:
- Models built once and rarely updated
- Assumptions that drift from real operating conditions
- Simulations disconnected from live data
- Results that can't be reused across teams or use cases
- High effort to modify or extend scenarios
When simulation is isolated from operations, it becomes a one-off exercise. When it's embedded in a digital twin, it becomes a continuous decision tool.
From model to decision
1. Model the system
Represent assets, processes, constraints, and interactions—discrete, continuous, or hybrid.
2. Set scenarios
Define inputs such as demand, schedules, failures, weather, policies, or control strategies.
3. Run simulations
Execute scenarios at speed to observe system behavior over time.
4. Compare outcomes
Evaluate trade-offs across cost, performance, risk, service, or safety.
5. Learn and refine
Feed results back into planning, optimization, and operational workflows.
Built for complex, real-world systems
Multi-paradigm simulation
Support discrete-event, continuous, and hybrid system modeling in one engine.
Constraint-aware execution
Respect real limits: capacity, resources, schedules, physics, and policies.
Scenario management
Create, version, and compare scenarios consistently across teams.
High-performance execution
Run many simulations quickly to explore alternatives—not just one "best guess."
Tight analytics & ML integration
Combine simulation with analytics and machine learning for prediction and optimization.
Learn more →Simulation applied to real decisions
Energy & sustainability
Evaluate load shifting, peak reduction, and emissions scenarios
Learn more →Simulation that stays aligned with reality
When simulation is part of the digital twin platform:
- Models stay synchronized with live operational data
- Assumptions are visible, testable, and updated continuously
- Results feed directly into analytics, prediction, and optimization
- Insights are reusable across planning, operations, and training
Simulation becomes a living capability—not a static study.
FAQ: Simulation engine
Test decisions before they carry real consequences
Use simulation as a daily advantage—not a one-time exercise.