Show HN: A small neural net asks if physical law is inevitable for any observer

Category: ai-ml

Tags: neural-networks, physics-discovery, cognitive-science, simulation, emergent-laws

Score: 7.0/10 (Innovation: 9, Technical: 8, Documentation: 5, Utility: 4)

This project uses small neural networks (GRU, LSTM, ViT, Transformer) as simulated 'finite observers' to explore whether physical laws are an inevitable emergent property of any system trying to predict reality from noisy, sequential data. It's interesting because it applies machine learning not just as a tool, but as a philosophical probe into the nature of observation, finding suggestive correlations (like EM vs. gravity clustering on an 'accumulation' axis) and recovering known physics (like 1/r potential) from prediction tasks alone.

Target audience: data engineers, ai researchers, physicists, cognitive scientists

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