Ridiculously fast symbolic expressions
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Updated
Jun 24, 2024 - Julia
Ridiculously fast symbolic expressions
Evolutionary Algorithms Framework
Vita - Genetic Programming Framework
High-Performance Symbolic Regression in Python and Julia
EC-KitY is a scikit-learn-compatible Python tool kit for doing evolutionary computation.
Physical Symbolic Optimization
Distributed High-Performance Symbolic Regression in Julia
Fit and evaluate nonlinear regression models.
Using deep symbolic regression for the discovery of a causal relationship between Williams coefficients and crack tip position using PhySO
Evolving Symbolic Pruning Metric from scratch
Mirror - Genetics Algorithms and Genetic Programming library. https://genetics4j.org
This is the official repo for the paper "LLM-SR" on Scientific Equation Discovery and Symbolic Regression with LLMs
Python bindings and scikit-learn interface for the Operon library for symbolic regression.
[ICLR 2024 Spotlight] SNIP on Symbolic Regression: Deep Symbolic Regression with Multimodal Pretraining
Mirror of https://bencardoen@bitbucket.org/bcardoen/csrm.git
[ICLR 2024 Spotlight] This is the official code for the paper "SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training"
C++ Large Scale Genetic Programming
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.
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