Current priority

Measured memory: earn the prediction.

Compare a specified physical-state model with conventional relaxation on independent data.

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Reproducible projects / Python standard library

Research you can pick up and run.

Three focused questions, three complete packages: code, labeled synthetic data, a blank measurement schema, a preregistration template, and captured expected outputs.

01Runnable project / synthetic demonstration

Geometry → resonance: predict the mode shift

Can a measured change in cavity length predict a resonant-frequency change under a specified conventional wave model?

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02Runnable project / synthetic demonstration

Retained physical memory: write, wait, probe

Does a defined physical probe distinguish trained and matched untrained states after a delay, and does passive relaxation explain the retained difference?

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03Runnable project / synthetic demonstration

Cross-scale relationships: challenge one exponent

Does a single dimensionless power-law relationship predict untouched observations better than a prespecified scale-break alternative?

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All supplied results are synthetic demonstrations. Running the code checks an analysis pipeline; empirical conclusions require independent observations and an appropriate uncertainty budget.