Workflow
Suggest
Looptimum proposes the next bounded trial using the current observation set instead of broad sweep scheduling.
Mechanics
A bounded loop with local state, explicit decisions, and low integration weight.
Looptimum does not replace your execution environment. It wraps it with a small, resumable trial-targeting controller.
Workflow
Evaluate
Your evaluator runs where it already lives: cluster jobs, scripts, CI runners, solver hosts, or lab workflows.
Workflow
Ingest
Results are recorded into local files so the loop resumes cleanly after interruptions and leaves an auditable trail.
Operational stance
Built for environments where the evaluator cannot be outsourced.
Looptimum keeps state and decision trace in local files so the loop survives interruptions, audit questions, and restricted deployment environments without requiring managed trial-targeting infrastructure.
Local state
Observations, trial manifests, and suggestion logs remain file-backed so the control plane stays transparent and resumable.
Thin contract
The evaluator only needs to accept bounded parameters and return a scalar objective or explicit failure outcome.
Failure-aware
Failed or timed-out evaluations are recorded rather than lost, which matters when each run is expensive.
Decision trace
The loop exposes why a trial was suggested and what had been learned so far, instead of hiding the state behind a service boundary.
Next step
See the workflow backed by a validated case.
The snappyHexMesh case shows the loop end to end: bounded campaign, archived decisions, and downstream solver validation.