Pure-Python Tool Rediscovering Kepler's Law from Just 8 Data Points
Pure-Python symbolic regression that rediscovered Kepler's law from 8 data point
I built GP_ELITE, a pure Python tool that uses genetic programming to find interpretable mathematical formulas from small datasets. Unlike black-box neural networks, it delivers readable laws, successfully rediscovering Kepler's Third Law with only eight data points. Designed for engineers and scientists, it requires no Julia or GPU, offering robust regression and built-in overfitting protection for real-world experimental data.
GP_ELITE's niche: zero barrier to entry. A lab engineer, a student, or a technician points at a CSV file and gets a validated law back — without becoming a developer.
- ziofill
The battery example makes no sense:
capacity_SOH ≈ 0.913 − 0.352 · tanh( cycle^((temperature/cycle)^0.485) )
I understand this fits the data, but exponents should be dimensionless, what is temperature/cycle?
- gus_massa
Some minor comments:
What happens if you give the system not only the semi-mayor axis but also the semi-minor axis?
Have you tried with only the 6 planets Kepler know? (I don't expect this to change the result too much.)
Have you tired with noisy data?
- sinuhe69
And I wonder if somebody has tried with the available galactic data and see if the genetic programming can come up with a better formula than MOND or Einstein's general relativity.
For simple problems as Kepler's law, a quick detour on Desmos will show a perfect fit for power law instantly. In general, there are many important criteria for a better curve fitting (for ex. independent, normal distributed residuals), not just R, so I hope the author has/will incorporate them into the search to create a more robust result.