Physics AI Data Pilot
Bring us one simulation workflow.
We'll explore what it takes to turn the underlying physical data into a reliable Physics AI asset.
You do not need to reorganize your entire engineering stack to evaluate Physics AI data infrastructure. A TecunTecs Physics AI Data Pilot begins with one bounded computational engineering problem.
Scope
Start with one simulation workflow.
Examples of a bounded scope
- one CFD workflow
- one FEA workflow
- one component family
- one parameter study
- one simulation archive
- one surrogate-model initiative
- one simulation-to-ML pipeline
Depending on the problem, a pilot can explore
- what data already exists
- whether it is suitable for ML
- what metadata and provenance are missing
- where quality problems exist
- how the data should be represented
- where parameter-space coverage is weak
- whether additional simulations should be generated
- how a repeatable simulation-to-AI data pipeline could work
The pilot is scoped around your engineering workflow. The objective is to leave you with a concrete, technically useful data asset and a clear path from simulation data to Physics AI.
Fit
Good pilot candidates.
A historical CFD archive accumulated across projects, teams, and solver versions.
A parameter sweep that already exists but was never organized as a dataset.
An existing surrogate-model project blocked by data quality or coverage.
A simulation workflow still managed through scripts and directories.
A dataset with uncertain quality or incomplete metadata.
A model that needs additional high-fidelity training cases.
Simulation and experimental data that need to be reconciled.
An engineering team exploring Physics AI but unsure how to prepare its data.
The process
How a pilot runs.
- 1
Understand the physical problem
Identify geometry, parameters, physics, solver environment, outputs, and intended AI use case.
- 2
Inspect the data
Understand what exists, how it is represented, what metadata survives, and where quality problems may exist.
- 3
Build the data path
Define transformations, validation, representations, and additional data-generation requirements.
- 4
Deliver a useful technical asset
The objective is not a slide deck. The objective is a technically useful artifact plus a clear path toward a scalable workflow.
One workflow is enough to start.
Bring us one simulation or Physics AI workflow. We'll explore what it would take to make the underlying data trainable, traceable, and reusable.