Automatic parametric modeling with symbolic regression
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Updated
Sep 21, 2026 - Python
Automatic parametric modeling with symbolic regression
Repository dedicated to the inference of physical laws from synthetic data using Symbolic Regression (SR) and Genetic Algorithms.
MCP server for symbolic regression — discover formulas from data, simplify expressions, check equivalence
A mathematical relation of the temperature, radius and luminosity with the Absolute Magnitude of a given star is derived using PySR library. Instead of building deep neural networks or complex ML algorithms, PySR simply tries to built mathematical expressions that best describe the relationship between variables in a dataset.
AI4S Agent assessing 14 organic-chemistry propositions with independent computation, layered evidence, and explicit scope boundaries.
Code for "Physics-Anchored Discovery of Trustworthy Pathloss Models": a discovery method whose output is physically valid and structurally deterministic, plus the evaluation tools and full experiment records.
This project was done as Master's Thesis in Institute of Particle Technology within TU Braunschweig. An autonomous LLM agent built using the Model Context Protocol (MCP) that optimizes symbolic regression on real industrial datasets from particle technology research.
Symbolic regression vs MLP on Kepler’s Third Law: a Law-Discovery Machine (LDM) using PySR. (Has Paper with it)
Using symbolic regression and genetic programming to discover an interpretable, closed-form equation for customer segmentation.
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