Surrogate model construction
Approximate an expensive simulator using a traceable design of experiments.
Activity responsibilities
| Activity | Responsibility |
|---|---|
design-samples | Execute the design samples stage and publish its declared outputs for downstream activities. |
run-simulation-batch-a | Execute the run simulation batch A stage and publish its declared outputs for downstream activities. |
run-simulation-batch-n | Execute the run simulation batch N stage and publish its declared outputs for downstream activities. |
assemble-dataset | Execute the assemble dataset stage and publish its declared outputs for downstream activities. |
train-surrogate | Execute the train surrogate stage and publish its declared outputs for downstream activities. |
validate-against-simulator | Execute the validate against simulator stage and publish its declared outputs for downstream activities. |
publish-model | Execute the publish model stage and publish its declared outputs for downstream activities. |
Inputs
- Simulator
- Parameter space
- Sampling policy
Outputs
- Simulation dataset
- Surrogate
- Validity report
Provenance to preserve
- Simulator version
- Samples
- Error by region
Source bundle
The executable workflow and its implementation files are maintained in the separate AkôFlow examples repository. The documentation intentionally keeps code, container recipes, generators, and validation scripts out of the narrative page.
Open the complete source bundle ↗Verified local execution
These captures and the output manifest were produced by the fixture's local Docker run and validator. They are published with the same bundle as the runnable files.
Execution considerations
Run simulations as HPC jobs and training on suitable accelerators. Preserve failed simulations and validity boundaries.
AkôFlow boundary: the engine schedules, deploys, executes, transfers data, and records evidence. The ML or agent framework remains an implementation choice inside each activity.