Ridiculously fast symbolic expressions
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Updated
Jun 4, 2024 - Julia
Ridiculously fast symbolic expressions
High-Performance Symbolic Regression in Python and Julia
Evolutionary Algorithms Framework
Physical Symbolic Optimization
Distributed High-Performance Symbolic Regression in Julia
C++ Large Scale Genetic Programming
Python bindings and scikit-learn interface for the Operon library for symbolic regression.
EC-KitY is a scikit-learn-compatible Python tool kit for doing evolutionary computation.
Univariate Skeleton Prediction in Multivariate Systems Using Transformers
SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
This is a Python library that implements a Multi-objective Symbolic Regression algorithm. It can be used as a Machine Learning algorithm to create predictive models in the form of mathematical expressions.
This is the official repo for the paper "LLM-SR: Scientific Equation Discovery via Programming with Large Language Models"
Machine learning library for symbolic fitting: the unknown system/function is described via NARMAX algebraic expressions being linear combinations of arbitrary non-linear terms provided by the user (like 0.2x²+0.7sin(x) or x[k-1]*y[k-4]^2).
Re-implementation of GP-GOMEA that attempts to be simpler to understand and use than the original.
Symbolic regression of physical models via Genetic Programming.
Fit and evaluate nonlinear regression models.
GNN and symbolic regression applied for active matter
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.
Vita - Genetic Programming Framework
Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
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