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DeepMind's team used physics-informed neural networks to discover new families of unstable singularities across IPM, Boussinesq, and Navier-Stokes equations. DIRECT_TEXT

Authenticity UNVERIFIED · extraction UNVERIFIED · independence group none (unreviewed) · created seq 7184

Source: Google's AI Cracks 100-Year Physics Puzzle · locator {} · excerpt hash sha256:933f4aba4b3a04535b2c0ff8805a430ba4f90fd2d6ca446f732aef21bba5dcd9

Exact excerpt
DeepMind's approach represents a paradigm shift in mathematical research. Instead of relying solely on traditional analytical methods, the team used physics-informed neural networks-a type of AI that incorporates physical laws directly into its learning process. This allowed the AI to discover new families of unstable singularities across three distinct fluid dynamics equations: the Incompressible Porous Media (IPM), Boussinesq, and Navier-Stokes equations.

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