Physical AI

Physical AI workflows need more than visual dashboards. Robots, equipment agents, and process models need structured scenes, physical constraints, operating rules, and repeatable feedback loops. FactVerse AI Agent connects FactVerse scenes, simulation-ready asset packages, simulation workflows, DFS-prepared operating data, and reviewed field evidence so industrial scenarios can be prepared and tested before real-site changes.

Operating context

Context	FactVerse source	Why it matters
Scene structure	Digital twin scenes, layouts, model assets, component geometry, twin-model bindings, and spatial relationships	Gives simulation and training workflows a usable world model
Simulation-ready assets	Model asset versions, component geometry, physical metadata, constraints, and scenario records	Keeps geometry, semantics, and physical assumptions tied together
Simulation workflow	Layout optimization, DES, what-if, Monte Carlo, system dynamics, building simulation, SimRunner, and project runtime adapters	Tests layout, motion, interaction, capacity, and process assumptions at iteration speed
Operating feedback	DFS data, Inspector execution records, work records, sensor history, and field validation notes	Links simulation assumptions with real operating evidence

Prerequisites and source data

• A reviewed FactVerse Designer scene with asset versions, layout boundaries, coordinate assumptions, and operating zones.
• Simulation-ready asset packages that carry geometry, metadata, physical constraints, and version history.
• DFS, Inspector, sensor, work-record, or field-validation inputs that describe real operating constraints.
• An engineering owner who can review assumptions, safety boundaries, scenario outputs, and downstream handoff.

Execution flow

Workflow

1. Select the scene, model asset versions, component geometry, layout, operating zones, and process context.
2. Prepare simulation-ready asset packages with geometry, metadata, constraints, coordinate assumptions, and version history.
3. Attach operating constraints through DFS, Inspector records, work records, sensor history, or reviewed field notes.
4. Run layout, DES, what-if, EnergyPlus-oriented, SimRunner, or project runtime experiments for layout, motion, sequence, process, or training context.
5. Compare results with real operating data and field validation notes.
6. Package findings for engineering review, robot training context, equipment training, process planning, or downstream runtime handoff.
7. Keep scenarios, assumptions, and validation results versioned for the next iteration.

Typical outputs

• Scenario packages with scene ID, asset versions, component geometry, coordinate assumptions, runtime parameters, and owner review.
• Layout and process comparisons with objective settings, changed parameters, KPIs, assumptions, and validation notes.
• Simulation records from DES, what-if, building simulation, SimRunner, or project runtime adapters.
• Training context references for robots, equipment agents, and operator training workflows.
• Feedback records that connect field results with later scene, asset, model, and prompt improvement.

Validation and failure handling

• Validate that each scenario records scene version, asset version, coordinate assumptions, simulation backend, and source data timestamps.
• Hold the workflow for engineering review when physical constraints, safety boundaries, or field evidence are incomplete.
• Compare simulation output against operating records or field validation before using it for real-site change planning.
• Keep rejected assumptions and revised parameters in the scenario history so later Physical AI iterations can improve.

Governance

Physical AI workflows should preserve the link between scene version, asset version, simulation assumptions, runtime parameters, and field validation. Fast simulation can improve iteration speed, while real-site deployment still requires engineering review, safety checks, and customer approval.

For the code-backed module reference, see Physical AI.