scientific-journal-writing

name	scientific-journal-writing
description	Use when writing, revising, outlining, or critiquing CFD-AI and scientific ML journal papers; focuses on argument structure, claim-evidence alignment, novelty, and reviewer defense.
version	0.4.0
author	CFD-AI Paper Skills maintainers
metadata	{"short-description":"Structure and sharpen CFD-AI/SciML journal manuscripts","gold-standard-authors":["Kai Fukami","Steven L. Brunton","Romit Maulik","Sangseung Lee","Ricardo Vinuesa"]}

Scientific Journal Writing for CFD-AI / SciML

Trigger

Use when the user asks for paper outline, abstract, introduction, methods, results, discussion, conclusion, novelty framing, contribution bullets, or manuscript critique for CFD-AI/SciML work.

Core rule

Do not merely improve English. First make the manuscript defensible.

A paper is defensible only if it has:

1. a physics/engineering problem,
2. a specific gap,
3. a calibrated contribution,
4. evidence for every major claim,
5. limitations before reviewers weaponize them.

Gold-standard writing target

Model the discipline of papers by Fukami, Brunton, Maulik, Lee, and Vinuesa:

• physics-first setup,
• ML role clearly scoped,
• reproducible numerical setup,
• fair baselines,
• physical diagnostics,
• generalization tests,
• figure sequence that proves claims,
• cautious wording.

The target is not “more academic English.” It is normal CFD-AI/SciML paper texture: physical problem first, narrow gap second, method role third, evidence sequence fourth, limitation boundary last. If the prose could fit any AI-for-science paper after replacing two nouns, it is not close enough to the gold-paper target.

Use progressive disclosure:

Task	Read first
Super-resolution/reconstruction abstract, intro, or results	references/gold-papers/fukami-2019-super-resolution-jfm.md
Spatio-temporal super-resolution	references/gold-papers/fukami-2021-spatiotemporal-super-resolution-jfm.md
Field positioning or related-work taxonomy	references/gold-papers/brunton-2020-machine-learning-fluid-mechanics.md and references/gold-papers/vinuesa-2022-enhancing-cfd-ml.md
ML workflow staging / physics insertion points	references/gold-papers/brunton-2021-applying-ml-fluid-mechanics.md
Uncertainty/trustworthy surrogate claims	references/gold-papers/maulik-2020-probabilistic-neural-networks-prf.md
Cylinder-wake prediction/generalization	references/gold-papers/lee-2019-cylinder-wake-jfm.md
Experimental-fluid-mechanics opportunity framing	references/gold-papers/vinuesa-2023-transformative-ml-experiments-nrp.md
Claim strength decisions	rubrics/claim-evidence-rubric.md
AI-ish / field-inappropriate vocabulary	references/field-terminology-style-guide.md, rubrics/vocabulary-style-rubric.md, examples/ai-ish-to-field-native-prose.md
Gold-paper closeness / normal paper texture	references/gold-paper-style-patterns.md, rubrics/gold-paper-closeness-rubric.md, examples/generic-ai-to-gold-paper-prose.md

Workflow

1. Classify manuscript state

Classify as:

• idea only
• outline
• partial draft
• full draft
• revision
• response letter
• LaTeX production

If missing, infer. Ask only when ambiguity blocks work.

2. Extract one-sentence claim

Use:

We propose X for Y under Z physical/numerical conditions, using W, and demonstrate A/B/C against baselines D/E.

If this cannot be written, stop polishing and diagnose the missing piece.

3. Contribution taxonomy

Classify each contribution:

• method/architecture
• physical modeling
• numerical algorithm
• surrogate/ROM/closure
• dataset/benchmark
• empirical physical insight
• uncertainty/verifiability
• software/workflow

Reject vague contribution bullets such as “we propose a novel framework.” Require mechanism + evidence.

4. Claim-evidence map

For every major claim, build:

Claim	Evidence needed	Current evidence	Risk
accurate	fair metrics + baselines	?	high if no baseline
physically consistent	conservation/residual/BC/spectrum	?	high
generalizes	unseen Re/geometry/BC/time	?	high
efficient	wall-clock/memory/params	?	medium
robust	noise/sparsity/OOD tests	?	high

Unsupported claims become TODOs, not prose.

5. Section logic

Abstract

Five moves:

1. physical/engineering context,
2. unresolved limitation,
3. proposed method,
4. concrete evidence/result,
5. implication/limited scope.

No “promising,” “robust,” “SOTA,” or “physically consistent” without measurable evidence.

Gold-paper closeness gate: the first sentence must name a physical/numerical object or bottleneck, not AI capability. Accept “high-resolution vorticity reconstruction from low-resolution snapshots”; reject “AI has revolutionized CFD.”

Introduction

Flow:

1. Why the fluid/process problem matters.
2. Why conventional CFD/experiments/current ML are limited.
3. Narrow gap.
4. Proposed approach.
5. Contribution bullets with evidence hooks.

Related work

Use taxonomy, not chronology:

• fluid-mechanics workflow role: discovery, modeling, sensing/reconstruction, experiments, closure, ROM, control, optimization, acceleration,
• ML insertion point: problem, data, architecture, loss, optimization, deployment/control,
• then method family: PINN, operator, CNN, GNN, ROM, sparse identification, RL, generative, or domain-specific model.

End with precise contrast.

Methods

Must include governing equations/assumptions, solver/data generation, BC/IC, nondimensional groups where relevant, architecture, loss, training, implementation details.

Results

Recommended sequence:

1. validation/sanity case,
2. main quantitative comparison,
3. field visualization GT/pred/error,
4. physics diagnostic,
5. ablation,
6. generalization,
7. efficiency,
8. failure/limitations.

Discussion

Explain why results matter physically. Do not restate tables.

Output template

Return:

1. One-sentence paper claim
2. Contribution bullets
3. Claim-evidence map
4. Section-level structure
5. Reviewer attack points
6. Required evidence/TODOs
7. Gold-paper closeness score, if writing/reconstruction is requested
8. Exact rewrite, if requested

For abstracts, use examples/bad-to-good-abstract.md as the style and evidence standard. For prose that sounds generic or AI-written, use references/gold-paper-style-patterns.md, rubrics/gold-paper-closeness-rubric.md, and examples/generic-ai-to-gold-paper-prose.md. When the user wants reviewer criticism to drive the rewrite, route through skills/paper-revision-loop-manager/SKILL.md and use templates/reviewer-editor-loop-report.md. For experiments, route to templates/experiment-plan.md and rubrics/sciml-experiment-rubric.md.

v0.4 eval-backed checks

These checks were added after simulated baseline-agent failures in evaluation/runs/2026-06-30-v04-*.

Trigger	Extra requirement
Spatio-temporal super-resolution	Separate spatial downsampling, temporal frame interval, high-resolution reference, and time-axis diagnostics.
Brunton-style taxonomy	Classify by workflow role before model family; gap paragraph must name CFD bottleneck, closest prior role, unresolved boundary, mechanism, and decisive evidence.
Cylinder/wake prediction	State Re/regime, split type, rollout horizon, and physical-consistency evidence before polishing prose.

Anti-patterns

• “novel” repeated as a substitute for novelty.
• “physics-informed” without saying where physics enters.
• “generalizable” from interpolation-only tests.
• “real-time” without hardware/timing.
• “state-of-the-art” without a fair baseline matrix.
• Generic opening context that could belong to any AI-for-science paper.
• Method-first paragraphs that name the network before the flow case, variable, and reference data.

Verification

Before finalizing:

• Every claim is supported or marked TODO.
• Unknown numerical details are not invented.
• Reviewer objections are explicit.
• Suggested text preserves scientific tone and does not overclaim.
• The final answer can be scored by rubrics/claim-evidence-rubric.md.
• If manuscript prose is generated, the final answer can also be scored by rubrics/gold-paper-closeness-rubric.md.