Repository dedicated to the inference of physical laws from synthetic data using Symbolic Regression (SR) and Genetic Algorithms.
Automated feature engineering
S. Guzey, and E. Hancer, “A Hybridized Feature Construction Method Based on Symbolic Transformers and Evolutionary Forest for Regression,” in Proc. 5th Int. Conf. Informatics and Software Eng. (IISEC), 2026.
A physics simulation framework for equation discovery using gplearn and symbolic regression
In my undergraduate thesis, I developed a novel feature construction method utilizing symbolic transformer and evolutionary forest algorithms. The work conducted throughout this process is available in this repository. My thesis, S. Guzey, and E. Hancer, “A Hybridized Feature Construction Method Based on Symbolic Transformers and Evolutionary Fores
My Data Engineering Master's project involved increasing the number of features in a dataset of R515B thermodynamic refrigerant using symbolic transformer and evolutionary forest methods to improve performance.
Emergentia is a neural-symbolic discovery engine that extracts parsimonious physical laws from noisy particle trajectory data. It combines deep learning to model complex forces with symbolic regression to rediscover human-readable, mathematically interpretable equations of motion.
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