BWhat changed
Proposed a joint asymptotic cost framework for CFD data generation and surrogate-model training, distinguishing RANS/LES fidelity, input/processor scaling and reuse of transient trajectories.
Scientific scope
Proposed a joint asymptotic cost framework for CFD data generation and surrogate-model training, distinguishing RANS/LES fidelity, input/processor scaling and reuse of transient trajectories. Conditional modeling framework, not an experimentally established universal CFD scaling law. Requires assumed mesh-error/learning power laws and an effective-sample hypothesis for trajectory reuse. Constants and representative estimates are approximate; no trained universal CFD foundation model or global optimality certified.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Conditional modeling framework, not an experimentally established universal CFD scaling law. Requires assumed mesh-error/learning power laws and an effective-sample hypothesis for trajectory reuse. Constants and representative estimates are approximate; no trained universal CFD foundation model or global optimality certified.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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