Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 293)

from Data Science at Home · host Francesco Gadaleta

VortexNet uses actual whirlpools to build neural networks. Seriously. By borrowing equations from fluid dynamics, this new architecture might solve deep learning's toughest problems—from vanishing gradients to long-range dependencies. Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI. Sponsors This episode is brought to you by Statistical Horizons At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible. Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons. Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com References https://samim.io/p/2025-01-18-vortextnet/

What this episode covers

VortexNet uses actual whirlpools to build neural networks. Seriously. By borrowing equations from fluid dynamics, this new architecture might solve deep learning's toughest problems—from vanishing gradients to long-range dependencies. Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI. Sponsors This episode is brought to you by Statistical Horizons At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible.Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons.Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com References https://samim.io/p/2025-01-18-vortextnet/

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Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 293)

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This episode was published on October 30, 2025.

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