What is recorded here?
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.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
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.
Read the original work
- Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics
- Mistral research publications index
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People and systems
Neil Ashton; Johannes Brandstetter; Siddhartha Mishra
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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English catalogue narrative
Fluid Intelligence: joint CFD-data and AI-training cost model
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
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.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Neil Ashton; Johannes Brandstetter; Siddhartha Mishra
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.