RQ-001
How much digital twin fidelity is actually required for predictive maintenance?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
Open Research Questions
Industrial Twin Lab publishes uncertainty instead of hiding it behind a product narrative. The initial catalogue is deliberately bounded to ten questions. Each question can accumulate hypotheses, experiments, negative results, replications, and revisions without being mistaken for a solved claim.
RQ-002
When does physics-informed feature engineering outperform end-to-end deep learning?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
RQ-003
Can synthetic failures improve models without introducing dangerous simulation bias?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
RQ-004
Which features generalize across machines of the same class?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
RQ-005
How should confidence from simulation, historical evidence, and machine learning be combined?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
RQ-006
Can an AI Scientist autonomously design useful industrial experiments while remaining outside the control loop?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
RQ-007
How should Digital Twin uncertainty propagate into AI recommendations?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
RQ-008
Can fleet learning work without centralizing industrial raw data?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
RQ-009
How can organizations distinguish correlation, causality, and physical mechanism?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
RQ-010
What evidence should be required before an industrial AI model is allowed into production?
Status: conceptual. A useful answer must state its asset class, operating envelope, evidence sources, uncertainty, and conditions under which it fails.
Industrial Twin Lab maturity model
Level 0 — Connected Asset
Machine → Data. Data access is known; no inference claim is made.
Level 1 — Observable Asset
Machine → Data → Monitoring. Signals and quality limits are visible and traceable.
Level 2 — Digital Twin
Machine ↔ Digital Representation. The representation is verified and validated for a stated use.
Level 3 — Twin Lab
Twin → Simulation → Experiments. Experiments are isolated, reproducible, and limitation-aware.
Level 4 — Machine Intelligence
Twin + Experiment Fabric + AI Scientist + Fleet Learning. Recommendations survive independent validation and explicit engineering review.
Maturity is not a software purchase. P-101 does not advance because a model was trained; it advances only when evidence supports a defined engineering decision across relevant regimes. A higher level also inherits every lower-level obligation: data quality, traceability, simulation validity, uncertainty, and human authority.