Simulation Operating System
A unified platform for building, composing, and executing complex simulations across physical, digital, and operational systems. Connect models, digital twins, and data into coordinated simulation environments.
Modern systems are too complex for isolated models. Energy, logistics, infrastructure, and economies are deeply interconnected.
Build unified digital representations of complex systems
Test scenarios before they happen in the real world
Analyze cascading effects across domains
Generate AI-powered recommendations from outcomes
Architecture
Sim-OS is built as a layered stack designed for interoperability, scalability, and governance.
The Foundation
Every simulation in Sim-OS is grounded by eVa (Elastic Vector Address) — the 5-dimensional coordinate system that binds models to precise locations, moments, and semantic realities.
Traditional simulation engines treat location, time, and context as separate concerns. eVa unifies them into a single, resolvable address — making every model spatially and temporally aware.
eva://<XYZ>@<FrameID>/<T>@<TimeAuthority>/<Plane>Example: A forklift in a warehouse simulation:
eva://47.6062,-122.3321,15.2@WGS84/2025-03-06T14:30:00Z@GPS/SIMULATIONKey Insight: The same XYZ coordinate can represent physical reality, a planned operation, or a simulation — distinguished only by the Plane dimension.
Three Degrees of Freedom
Coordinates are frame-agnostic until bound to a reference frame.
Timestamp + Clock Source
Time is always bound to an authority. Different clocks define different realities.
Semantic Reality Layer
Defines what kind of reality or meaning. Separates simulated from physical.
When you compose a simulation in Sim-OS, every model, asset, and data stream is automatically bound to an eVa address. This enables:
Simulation Address Example
This address uniquely identifies a point in a logistics simulation, separate from the physical warehouse at the same XYZ.
Components
Key building blocks that enable simulation at scale.
MoE
Spatial search engine and natural language interface for model discovery
SoI
Policy boundaries ensuring models operate within defined authority domains
Model Ontology
Semantic classification by paradigm: physics, agent-based, ML, stochastic
Model Library
Central repository with versioning, metadata indexing, and AI synthesis
Model Mesh
Connects models through standardized interfaces and automatic adapters
Simulation Kernel
Core execution engine with deterministic scheduling and monitoring
Rig (Digital Twin)
Unified environment combining assets, infrastructure, and models
Time Engine
Temporal management: real-time, accelerated, historical replay
Digital Twins
Your entire operation rendered as an interactive digital twin, powered by spatially-aware simulation and real-time data integration.
Live view of all assets, people, and processes
Test changes before implementing in production
SoI defines operational boundaries and constraints
Interoperability
Understanding the difference between model interoperability and machine learning within Sim-OS.
Lightweight components that transform model outputs for downstream consumption.
Key: Deterministic, lightweight, interoperability layer
Machine learning models for pattern recognition, prediction, and generative reasoning.
Key: Intelligence layer, works with adapters for complex simulations
Workflow
Explore models and assets through MoE
Connect models from the library
Run across Mission Fabric
Review simulation outcomes
Outputs
Structured reports supporting both human decision-makers and automated systems.
Applications
Sim-OS enables simulation across many domains.
Cascading failures in energy and transportation
Disruption analysis and route optimization
Operational scenarios and strategies
Climate systems and ecosystems
City infrastructure and population dynamics
Capabilities rarely found in a single platform:
As systems grow more interconnected, the ability to simulate them before acting becomes essential. XRDNA Sim-OS provides the foundation for next-generation modeling and simulation.