About Simulation Execution Standard - (SES)
About Simulation Execution Standard (SES)
Architecture: SIMULOS® — Simulation Governance Architecture Category:Governance & Enforcement Subcategory: Simulation Governance Parent Standard:
Simulation Execution Standard (SES) Governed Space: Simulation Execution
Operational Space: Simulation Execution Governance Governance Flow Position: 8
of 12 — Simulation Scenario → Simulation Execution → Simulation Output
Governance
Canonical Definition
Simulation Execution Standard (SES) defines the governance conditions underwhich a governed simulation is instantiated, executed, controlled, observed,
repeated where required, and documented so that the actual execution remains
traceable to the Simulation Object, model, environment, scenario, inputs,
parameters, configuration, runtime conditions, interventions, deviations, and
outputs that formed the execution event.
SES governs the point at which a designed simulation becomes an actual
simulation execution.
It ensures that organizations can distinguish between:
what was intended to run
and:
what actually ran.
Why SES Matters
Modern organizations may execute thousands or millions of simulations acrossAI systems, autonomous systems, robotics, digital twins, engineering
environments, and mission systems.
The challenge is therefore not only whether simulation capability exists.
The governance question becomes:
Can the organization reconstruct exactly what happened during a materially
relevant simulation execution?
SES provides the structure for answering:
Which configuration actually ran?
Which model and environment versions were used?
Which scenario, inputs, and parameters were instantiated?
What happened during execution?
Were there interventions, interruptions, failures, or deviations?
Was the simulation repeated under comparable conditions?
Can the resulting outputs be traced to the execution that produced them?
What SES Governs
SES governs five connected execution spaces:Simulation Instantiation Establishes the exact executable simulation instance
and binds it to its relevant configuration.
Simulation Execution Control Governs how execution is initiated, progressed,
controlled, interrupted, modified where permitted, terminated, and completed.
Simulation Observation Preserves materially relevant states, events,
transitions, anomalies, interventions, and runtime conditions.
Simulation Repetition Governs repeated runs, randomization, repeatability,
reproducibility conditions, execution variance, and comparability.
Simulation Execution Documentation Creates the traceable record needed to
reconstruct what actually occurred.
Governance Flow
Governed Scenario & Simulation
Configuration
↓Simulation Instantiation
↓Execution Control
↓Simulation Observation
↓Simulation Repetition
↓Execution Documentation
↓Governed Simulation Execution Record
↓Simulation Output Governance What SES Does
Not Determine
SES does not determine whether: the Simulation Object was correctly selected,the representation perfectly reflects reality, the simulation assumptions are
universally valid, the simulation output is correct, an inference from the
output is justified, the simulated system is safe, or the system should be
deployed.
Those questions belong to other SIMULOS® spaces or compatible OOF®
architectures.
SES governs specifically:
the execution event and its reconstructability.
Enterprise Application
SES can operate above an organization's existing simulation infrastructure.A company does not need to replace its: simulation engine, robotics simulator,
digital twin, Monte Carlo environment, HPC infrastructure, AI evaluation
environment, engineering simulator, or proprietary simulation platform.
SES provides a governance layer for examining whether simulation execution
remains identifiable, controlled, observable, repeatable where required,
documented, and traceable.
This makes SES applicable both as an execution governance framework and as a
diagnostic structure for identifying execution-governance gaps in existing
simulation environments. Use Case 1 — Autonomous System SimulationScenario
A company performs thousands of simulations before deploying an autonomous
robotic system.
Several runs show unexpected collision-avoidance behavior, but engineers
cannot reliably determine whether the difference originated from: model
version, environment configuration, sensor inputs, randomization, parameter
changes, runtime intervention, or actual autonomous behavior.
SES Application
SES binds each material execution to its:Execution ID → Model → Environment → Scenario → Inputs → Parameters → Runtime
Conditions → Observations → Outputs
Repeated runs remain distinguishable, while material execution deviations and
interventions remain visible. Result
The organization can reconstruct the simulation conditions associated with the
anomalous behavior and determine which executions are legitimately comparable
before downstream interpretation or validation begins. Use Case 2 — Space
Mission Simulation CampaignScenario
A space organization executes a large mission-simulation campaign covering
combinations of: navigation uncertainty, communication loss, sensor
degradation, power limitations, subsystem failures, autonomous recovery, and
changing environmental conditions.
A critical mission decision depends upon results generated across a very large
population of simulation runs.
SES Application
SES establishes execution and campaign traceability so that materiallyrelevant results remain connected to:
Scenario → Configuration → Execution Conditions → Runtime Events → Repetition
→ Completion Status → Output
Interrupted, failed, modified, or anomalous runs remain visible rather than
disappearing from the analytical record. Result
Decision-makers can determine not merely that simulations were performed, but
which configurations actually executed, under which conditions, whether
execution remained complete and controlled, and which exact executions
produced the outputs being relied upon.
Business Value
SES can help organizations identify potential governance gaps such as:Execution Traceability Gaps
Configuration-to-Run Gaps
Observation Gaps
Intervention Traceability Gaps
Execution Drift
Repetition Comparability Gaps
Failed-Run Visibility Gaps
Execution-to-Output Lineage Gaps
These findings can then be investigated before conclusions are drawn aboutsimulation quality, validity, safety, or operational readiness.
Relationship to Adjacent SIMULOS®
Standards
Simulation Scenario Standard (SSS) asks:What scenarios must be simulated?
Simulation Execution Standard (SES) asks:
What actually happened when those simulations were executed?
Simulation Output Governance Standard (SOGS) then asks:
What exactly did those executions produce, and what may legitimately be
inferred from those outputs?
The architectural transition is therefore: