signal新智元2026-10-06
Tao Zhexuan's Late-Night Endorsement: Caltech Physical AI Solves Century-Old Problem Ahead of OpenAI's Release
Caltech's team led by Anima Anandkumar used physics-informed neural networks (PINN) to obtain the first candidate singularity for the unforced 3D Euler equations, with the scaling exponent converging to 0.5 as theoretically predicted. They overcame previous failures by allowing moving singularities, loosening parity constraints, and using custom optimizers (SS-eSOAP and SS-Broyden). Tao Zhexuan endorsed the work, highlighting its significance for AI for Science.
- for who
- Mathematicians, physicists, and AI researchers interested in AI-driven scientific discovery.
- what changes
- AI is now seen as a complement to human intuition, not a replacement, in solving complex scientific problems.
key points
- Caltech team uses PINN to find first candidate singularity for unforced 3D Euler equations
- Scaling exponent converges to 0.5, matching theoretical prediction by Constantin
- Anima Anandkumar advocates AI to complement human abilities, not compete
#physical ai#euler equations#pinn#ai for science
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