ziatdinovmax/gpax
Gaussian Processes for Experimental Sciences
This tool helps scientists reconstruct and analyze experimental data, especially when measurements are sparse or incomplete. You input your existing experimental data, and it outputs a complete, reconstructed dataset or image, along with an understanding of the uncertainties in those reconstructions. It's designed for researchers, experimental physicists, and materials scientists who need to interpret complex scientific measurements and predict outcomes.
233 stars. No commits in the last 6 months.
Use this if you are working with experimental science data and need to reconstruct missing information, denoise measurements, or incorporate your existing physical knowledge into data analysis to get more accurate predictions and uncertainty estimates.
Not ideal if your data is purely observational without underlying physical principles, or if you primarily need a simple, black-box predictive model without incorporating complex prior knowledge.
Stars
233
Forks
30
Language
Python
License
MIT
Category
Last pushed
Jul 04, 2025
Commits (30d)
0
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