Discovering causal relations and equations from data
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …
questions about why natural phenomena occur and to make testable models that explain the …
Artificial intelligence in physical sciences: Symbolic regression trends and perspectives
Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …
programming principles that integrates techniques and processes from heterogeneous …
Interpretable machine learning for science with PySR and SymbolicRegression. jl
M Cranmer - arxiv preprint arxiv:2305.01582, 2023 - arxiv.org
PySR is an open-source library for practical symbolic regression, a type of machine learning
which aims to discover human-interpretable symbolic models. PySR was developed to …
which aims to discover human-interpretable symbolic models. PySR was developed to …
[HTML][HTML] Contemporary symbolic regression methods and their relative performance
Many promising approaches to symbolic regression have been presented in recent years,
yet progress in the field continues to suffer from a lack of uniform, robust, and transparent …
yet progress in the field continues to suffer from a lack of uniform, robust, and transparent …
Kan: Kolmogorov-arnold networks
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold
Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs …
Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs …
Kan 2.0: Kolmogorov-arnold networks meet science
A major challenge of AI+ Science lies in their inherent incompatibility: today's AI is primarily
based on connectionism, while science depends on symbolism. To bridge the two worlds …
based on connectionism, while science depends on symbolism. To bridge the two worlds …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
A unified framework for deep symbolic regression
The last few years have witnessed a surge in methods for symbolic regression, from
advances in traditional evolutionary approaches to novel deep learning-based systems …
advances in traditional evolutionary approaches to novel deep learning-based systems …
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
Symbolic regression (SR) is the study of algorithms that automate the search for analytic
expressions that fit data. While recent advances in deep learning have generated renewed …
expressions that fit data. While recent advances in deep learning have generated renewed …
Combining data and theory for derivable scientific discovery with AI-Descartes
Scientists aim to discover meaningful formulae that accurately describe experimental data.
Mathematical models of natural phenomena can be manually created from domain …
Mathematical models of natural phenomena can be manually created from domain …