related-work-synthesis

name	related-work-synthesis
description	Use when searching, reading, organizing, and writing related work for CFD-AI/SciML papers, with emphasis on taxonomy, close-prior-art risk, and gap positioning.
version	0.4.0
author	CFD-AI Paper Skills maintainers
metadata	{"short-description":"Related-work taxonomy and gap synthesis for CFD-AI/SciML"}

Related Work Synthesis for CFD-AI / SciML

Trigger

Use for literature review, related work, prior-art risk, “how is my work different?”, finding journal references, and citation positioning.

Workflow

1. Define axes

Identify:

• physical domain,
• task: prediction/surrogate/closure/reconstruction/control/discovery,
• ML family: PINN/operator/CNN/GNN/generative/ROM,
• data regime,
• target journal community.

2. Search families

Use multiple query types:

• exact method terms,
• physical-domain terms,
• competing method terms,
• review/survey terms,
• target-journal terms,
• gold-author names: Fukami, Brunton, Maulik, Lee, Vinuesa.

3. Extract metadata

For each paper:

• title/authors/year/venue,
• DOI/arXiv/URL,
• problem setting,
• method,
• evidence,
• limitation,
• relevance,
• how close it is.

4. Build taxonomy

Prefer categories:

First classify by fluid-mechanics workflow role:

• discovery/governing equations,
• modeling/surrogate/acceleration,
• sensing/reconstruction/super-resolution,
• experiments/design/digital twins,
• turbulence closure/ROM,
• graph/mesh/geometric modeling,
• uncertainty/verifiability,
• control/optimization,
• domain-specific wafer/CVD/process models.

Then classify by ML family: PINN, neural operator, CNN/U-Net, GNN, sparse identification, ROM/latent dynamics, RL, generative, or statistical surrogate.

Use progressive disclosure:

Need	Read
Workflow taxonomy	references/gold-papers/brunton-2020-machine-learning-fluid-mechanics.md
Sparse discovery	references/gold-papers/brunton-2016-sindy-pnas.md
ML workflow staging	references/gold-papers/brunton-2021-applying-ml-fluid-mechanics.md
CFD opportunity map	references/gold-papers/vinuesa-2022-enhancing-cfd-ml.md
Experiments	references/gold-papers/vinuesa-2023-transformative-ml-experiments-nrp.md

5. Prior-art risk

Mark papers as:

• background,
• adjacent,
• close,
• dangerous close prior art.

Dangerous close prior art requires explicit differentiation or experiment.

6. Gap paragraph

Use:

Prior work primarily addresses X under Y conditions using Z. However, A remains unresolved, especially for B. The present work targets A by C and evaluates it through D.

No fake novelty. If the work is incremental, frame it honestly.

Output template

1. Search scope
2. Taxonomy
3. Key papers
4. Dangerous close prior art
5. Gap statement
6. Draft related-work paragraphs
7. Missing citation/BibTeX TODOs

Add:

Paper	Workflow role	ML insertion point	Prior-art risk	Source scope

Gap paragraph must name the CFD bottleneck, closest prior role, unresolved boundary, proposed mechanism, and decisive evidence.

Anti-patterns

• Organizing related work as a citation list instead of a taxonomy.
• Treating all ML-for-CFD papers as interchangeable.
• Overstating novelty without naming closest prior art.
• Filling missing bibliographic details from memory.
• Sorting papers only by model family without workflow role.

Verification

• Do not cite papers not checked.
• Keep URLs/DOIs/arXiv IDs with claims.
• Mark uncertain claims.
• Separate background from close prior art.
• Every paper has source scope: full text, abstract only, metadata only, or TODO.