TransferMod v1.2.1 treats empirical scope as an open question rather than an assumption.
| Track | Current evidence | Interpretation |
|---|---|---|
| Learned operators | the trained random-feature diffusion operator shows no point with relative global L2 error at or below 0.01 and decision error at or above 0.02 | negative result for decoupling in this model/family; aggregate and decision degradation remain coupled |
| Higher-dimensional PDEs | compact 2D/3D uniform and coarse-to-fine searches execute successfully | implementation-feasibility result only; production solver scaling remains open |
| Bayesian Silent Risk | conjugate drift–diffusion posterior cleanly separates posterior weighting, anchored fidelity, worst-case fidelity, and decision diameter | successful implementation validation, not evidence of broad posterior robustness |
Run the frozen protocol in LEARNED_OPERATOR_PROTOCOL.md on externally trained
operators that were neither designed nor trained for TransferMod.
The first confirmatory study should include:
- publicly released FNO and DeepONet checkpoints;
- model and checkpoint hashes fixed before analysis;
- predeclared aggregate metrics, quantities of interest, thresholds, families, and search budgets;
- complete reporting of coupled, decoupled, and inconclusive outcomes;
- no post-result substitution of perturbation families in the confirmatory analysis.
- external learned-operator replication;
- solver-coupled two- and three-dimensional PDE studies with measured hardware cost;
- non-conjugate, multimodal, and MCMC posterior Silent Risk;
- preregistered comparative studies across models, metrics, quantities, and information bases.
The scope question is now explicit: under what conditions do aggregate and decision fidelity decouple in real trained systems? A negative answer for a particular model–metric–quantity–family combination is a first-class result.