Using Neural Networks to Test Whether Physical Laws Emerge from Observer Constraints
A researcher trained a small GRU network on real astrophysical data to explore whether physical laws are a structural consequence of finite observation rather than intrinsic facts about reality. The experiment shows that memory accumulation requirements vary systematically across physics domains, and that a trained observer recovers gravitational potentials without explicit physics in the training signal. This is a speculative research project with minimal engagement, not a software problem statement.
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