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DeepMind and mathematicians use neural networks to find new unstable singularities in fluid equations

★★★scienceGoogle DeepMindNew York UniversityStanford UniversityBrown Universityconfidence: high

A DeepMind-led team (with Tristan Buckmaster and Javier Gómez-Serrano) used physics-informed neural networks and high-precision optimisation to find new families of unstable self-similar blow-up solutions for the incompressible porous media and Boussinesq equations (3D Euler with boundary), accurate to near machine precision. This was a numerical discovery, not a proof.

Key facts

Science result

Field
mathematics / partial differential equations / fluid dynamics
Problem
Finite-time singularity formation in fluid equations (Euler, Boussinesq, IPM)
Result
Discovery of previously unknown families of unstable self-similar blow-up solutions, computed to near machine precision.
AI system
physics-informed neural networks with Gauss–Newton optimisation
Human role
Human-led with AI tools: co-designed by mathematicians
Verification
Numerical; preprint; not a rigorous proof
Status
confirmed

What happened

Unstable singularities are thought to be what any Navier–Stokes blow-up would look like, but they are almost impossible to find numerically. The team's neural-network method found whole families of them.

Why it matters

It was the groundwork of the AI-plus-computer-assisted-proof approach to fluid blow-up that culminated in the disputed 2026 Navier–Stokes claims.

Changelog

  • 2026-09-29: created

Related events

  1. OpenAI claims a Millennium Prize problem: 10,000 AI agents prove forced Navier–Stokes blow-up; priority dispute erupts ★★★★★
  2. Caltech team (Anandkumar) reports a stable self-similar singularity candidate for the unforced 3D Euler equations on R³, found with PINNs and LLM help ★★★

Sources (2)

id: 2025-09-17-deepmind-unstable-singularities-fluids · updated 2026-09-29 · open in the interactive timeline