RUNTIME SIMULATION ARCHITECTURE
A persistent, multi-instance simulation engine designed to support training, engineering, and operational scenarios from the same runtime.
Endeavor’s platform is built around a runtime-first simulation architecture.
Instead of executing simulations as isolated studies, the system operates as a continuous, deterministic runtime capable of hosting multiple concurrent scenarios, users, and data streams. This architectural decision fundamentally changes how simulation is used—shifting it from a static workflow into a scalable system layer.
Most simulation platforms were designed around a single-study execution model.
Each scenario is configured, run, exported, and analyzed as a standalone event.
That approach introduces structural limits:
- One execution context per study
- Offline pre-processing and post-processing dependencies
- Separate tools for training, engineering, and operations
- No persistent system state across scenario
These constraints are not configuration issues. They are architectural.
Endeavor removes these limits by treating simulation as a persistent runtime, not a sequence of jobs.
Endeavor’s engine maintains a continuous world state that can be branched, modified, and executed in parallel without rebuilding or restarting the simulation environment.
Scenarios are forked from the same runtime foundation, preserving deterministic physics while allowing variation in conditions, parameters, and operator behavior.
This enables training sessions, engineering studies, and operational what-if analysis to coexist within the same execution model.
Multi-instance execution
Run multiple scenarios concurrently from a shared runtime.
Studies are branched rather than rebuilt, allowing rapid exploration of outcomes without resetting the simulation state.
Enables:
- Parallel what-if analysis
- Instructor-led scenario branching
- Faster iteration without workflow resets
Deterministic physics
Maintain a consistent ground truth across runs.
Under identical inputs, the engine produces repeatable outcomes—critical for evaluation, benchmarking, and safety-critical environments.
Enables:
- Comparable performance assessment
- Auditability and repeatability
- Standardized training outcomes
Real-Time Data Integration
Connect simulation to live systems.
The runtime can ingest real-time inputs from PLCs, sensors, or operational data feeds, and output computed or synthetic signals back to connected systems.
Enables:
- Live digital twin alignment
- Hardware-in-the-loop testing
- Synthetic data generation
Multi-User Orchestration
Support complex roles within a simgle simulation.
Up to 100 connected devices can interact with one runtime, including instructors, trainees, engineers, supervisors, and observers—all operating on a synchronized state.
Enables:
- Team-based training
- Realistic operational dynamics
- Instructor control without interupting execution
Traditional simulation workflows are linear and segmented.
Conventional approach:
- Configure scenario
- Execute single run
- Export results
- Analyze offline
- Repeat
Runtime-driven approach:
- Execute live
- Branch scenarios instantly
- Modify conditions during execution
- Observe outcomes in real time
- Evaluate within the same environment
The gain is not just speed. It is structural efficiency.
Because the platform is runtime-centric, new use cases emerge without rewriting the core system
- Adaptive training
Live scenario branching with repeatable assessment - Accelerated engineering studies
Parallel execution of condition variations - Operational digital twins
Real-time alignment between simulation and field data - Hardware-in-the-loop testing
Closed-loop interaction with external systems - Synthetic data generation
Controlled outputs for analytics and AI workflows
ENGAGING WITH ENDEAVOR
Let's discuss your use case
Training, operations, or simulation architecture—start with a focused discussion on requirements and deployment context.