AI finds a candidate; completing the proof comes next
- What it is
- The paper presents AI-guided evidence and a proposed route toward a stable singularity in ideal fluid flow.
- Who did it
- Adarsh Ganeshram, Valentin Duruisseaux, and Anima Anandkumar
- What it could mean
- An AI may have spotted the mathematical moment a smooth fluid model breaks. Finishing the proof would turn that machine-found pattern into something researchers can actually rely on.
See the check plan
Evidence & validation
From announcement to evidence
Discovery recorded. State of Proof has not yet examined this claim.
Read the original work
Stable Singularity of the Euler Equations on R³ ↗See the proposed checks
Which quantitative estimates and interval certificates are complete, and which stability obligations are still open?
No proof docket yet
A docket is the public record of checks and open questions. This paper does not have one yet; the check plan above describes work still to do.
Explore existing proof dockets →
- What it claims
- The manuscript presents evidence of a stable finite-time singularity, an approximate profile discovered with a physics-informed neural network, and a framework reducing nonlinear stability to finite quantitative estimates.
- Why this could matter
- AI finds a candidate; completing the proof comes next A neural network finds an approximate pattern that could become a singularity in ideal fluid flow. The manuscript offers evidence and a stability framework, but explicitly lists unfinished proof work. Finding a promising pattern is not yet proving it exists.
- If it holds up
- Completing the quantitative certification could turn AI-guided discovery into a rigorous singularity result under the exact stated assumptions.
- If it does not
- An unsuccessful certification would reveal where the approximate pattern or stability estimates need to change.
- Impact horizon
- Methods · AI-assisted discovery · Fluid models · Computer-assisted proof
- Version
- Public manuscript retrieved 2026-09-08; PDF SHA-256 f0164c40fad09a646412acec95f7908ea6b2fd61d16b809954a4048665fb5f78. Discovery date is not a claim of first publication.
- Why we tracked it
- The September 8 fluid-mathematics announcements warrant distinct intake records for each equation, forcing assumption and proof-completion state.
- Highest-risk dependency
- The manuscript explicitly lists remaining work to complete the proof. Do not label this a completed Euler solution or equate partial certification with the full theorem.
- Available artifacts
- No formal replay artifact was established in this bounded intake. A physics-informed neural network is central to profile discovery. The paper limits the role of language models to supporting tasks. No manuscript-linked code was executed; source availability is not proof verification.
- Current boundary
- Intake record only; examination not started.