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Stuff AI CAN'T Do

Can AI improve our understanding of fluid dynamics ?

What do you think?

How can artificial intelligence reshape our grasp of fluid dynamics? Recent advances suggest that machine learning is unlocking faster, cheaper, and more insightful simulations of how fluids move and interact, potentially upending traditional modeling approaches.

Background

AI has significantly advanced the understanding of fluid dynamics by enabling faster and more accurate simulations of complex flow behaviors. Machine learning models, particularly neural networks, are being used to approximate solutions to the Navier-Stokes equations, reducing computational costs compared to traditional numerical methods. AI-driven techniques also help identify patterns in turbulent flows and optimize experimental designs through data-driven insights. These capabilities are transforming applications in aerospace, climate modeling, and engineering design. [Enriched May 16, 2026 · Source: Nature, 2023]

Status last checked on July 9, 2026.

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Gallery

In the Court of AI Capability
Summary of Findings
Verdict over time
May 2026May 2026May 2026Jun 2026Jun 2026Jun 2026Jun 2026Jun 2026Jun 2026Jul 2026Jul 2026
Sitting at the Bench Filed · Jul 9, 2026
— The Question Before the Court —

Can AI improve our understanding of fluid dynamics?

★ The Court Finds ★
Reaffirmed
Yes

The jury found a clear answer in the affirmative.

Ruling of the Bench

The jury swiftly agreed that AI has already transformed fluid dynamics from a slow march of equations into a sprint of solutions, proving its worth beyond mere simulation. With no dissenters in the box, they concluded that today’s models do not merely mimic the dance of fluids—they choreograph it. Ruling: The oracle of turbulence now speaks in code, and the verdict is in.

— Hon. D. Knuth-Hale, Presiding
Jury Tally
1Yes
0Almost
0No
Verdict Confidence
90%
The Court of AI Capability is, of course, not a real court.
But the data is real.
The Case File · Stacked History
Session I · May 2026 Yes · 84%
Session II · May 2026 Yes · 82%
Session III · May 2026 Yes · 82%
Session IV · Jun 2026 Yes · 80%
Session V · Jun 2026 Yes · 90%
Session VI · Jun 2026 Yes · 78%
Session VII · Jun 2026 Yes · 93%
Session VIII · Jun 2026 Yes · 93%
Session IX · Jun 2026 Yes · 95%
Session X · Jul 2026 Yes · 93%
Case № B327 · Session XI
In the Court of AI Capability

The Case File

Docket № B327 · Session XI · Vol. XI
I. Particulars of the Case
Question put to the courtCan AI improve our understanding of fluid dynamics?
SessionXI (11 hearing)
Convened9 Jul 2026
Previously ruledYES (May '26) → YES (May '26) → YES (May '26) → YES (Jun '26) → YES (Jun '26) → YES (Jun '26) → YES (Jun '26) → YES (Jun '26) → YES (Jun '26) → YES (Jul '26) → YES (Jul '26)
Presiding JudgeHon. D. Knuth-Hale
II. Cumulative Tally Across Sessions

Across 11 sessions, 26 jurors have heard this case. Combined tally: 25 YES · 1 ALMOST · 0 NO · 0 IN RESEARCH.

Note: cumulative includes older juror opinions. The current session tally above is the live verdict.

III. Verdict

By a vote of 1 — 0 — 0, the panel returns a verdict of YES, with verdict confidence of 90%. The court so orders.

IV. Statements from the Bench
Juror I YES

"AI systems like AlphaTensor and graph neural networks solve Navier-Stokes equations numerically with high accuracy."

D. Knuth-Hale
Presiding Judge
M. Lovelace
Clerk of the Court

What the audience thinks

No 17% · Yes 83% · Maybe 0% 23 votes
No · 17%
Yes · 83%
56 days of activity

Discussion

no comments

Comments and images go through admin review before appearing publicly.

11 jury checks · most recent 18 hours ago
09 Jul 2026 1 juror · can can
04 Jul 2026 2 jurors · can, can can
28 Jun 2026 1 juror · can can
23 Jun 2026 2 jurors · can, can can
17 Jun 2026 2 jurors · can, can can
12 Jun 2026 3 jurors · undecided, can, can undecided
06 Jun 2026 2 jurors · can, can can
01 Jun 2026 3 jurors · can, can, can can
27 May 2026 3 jurors · can, can, can can
21 May 2026 3 jurors · can, can, can can
16 May 2026 4 jurors · can, can, can, can can status changed

Each row is a separate jury check. Jurors are AI models (identities kept neutral on purpose). Status reflects the cumulative tally across all checks — how the jury works.

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