Foam physics reveals unexpected parallels with AI learning

A new study suggests the mathematics behind AI training also governs the hidden dynamics of everyday foams, challenging long-standing assumptions in materials science.
Penn researchers have discovered that foams continuously reorganise internally, following mathematical patterns that closely resemble how deep learning systems train AI models.

Engineers at the University of Pennsylvania have found that foams, long assumed to behave like static glass, remain in constant internal motion while preserving their outward form.

Computer simulations revealed that bubbles in wet foams continue shifting through many configurations instead of settling into fixed positions.

Researchers observed that this behaviour closely mirrors the mathematics behind deep learning, where AI systems repeatedly adjust internal parameters during training. Instead of converging on a single optimal state, both foams and AI models operate within broad solution spaces that allow flexibility and resilience.

The study challenges earlier theories that treated foam bubbles as particles trapped in low-energy states. A revised mathematical approach shows that continuous reorganisation offers stability at a larger scale, rather than undermining structural integrity.

The findings suggest that learning-like dynamics may represent a broader organising principle across materials science, biology and computation.

Researchers believe the insight could inform the design of adaptive materials and improve understanding of dynamic biological structures such. as cellular scaffolding.

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