Observe
Collect typed outcomes, failures, uncertainty, artifacts, and runtime facts from simulations or physical tests.
Capabilities
Closed-loop optimization
Every result changes what the system asks next.
Join simulation, optimization, experiments, manufacturing data, and model correction in one governed learning loop.Scope
Adaptive campaigns under explicit authority
Governing principle
Optimizers propose. Evidence and policy determine what advances.
Close the loop without surrendering control.
A high-value design program does not end when a solver returns. Results should update uncertainty, invalidate assumptions, reshape the candidate distribution, trigger higher-fidelity analysis, and eventually absorb test or as-built evidence. AeroGalactica keeps that feedback durable while separating numerical proposal from decision authority.
System logic
A learning decision cycle
Update
Revise campaign state, surrogate models, confidence, and the evidence graph without rewriting history.
Propose
Generate the next candidates within approved design spaces, budgets, capabilities, and stopping rules.
Promote
Advance only candidates that satisfy review gates, validation criteria, and the intended use of the decision.
Failures become information.
Non-convergence, invalid geometry, exhausted budgets, and violated constraints remain typed outcomes that inform the next step.
Fidelity can escalate deliberately.
Fast models screen broad spaces while selected candidates advance to expensive simulations, experiments, or independent validation.
Physical evidence can return to the model.
Test data, manufacturing measurements, and as-built behavior can correct assumptions and start the next design cycle with a better state.
The result is a system that learns from computation and reality while keeping every consequential promotion reviewable.
Continue through the system