Machine learning conservation laws from trajectories
We present AI Poincaré, a machine learning algorithm for autodiscovering conserved
quantities using trajectory data from unknown dynamical systems. We test it on five …
quantities using trajectory data from unknown dynamical systems. We test it on five …
Machine learning hidden symmetries
We present an automated method for finding hidden symmetries, defined as symmetries that
become manifest only in a new coordinate system that must be discovered. Its core idea is to …
become manifest only in a new coordinate system that must be discovered. Its core idea is to …
Information fusion via symbolic regression: A tutorial in the context of human health
JJ Schnur, NV Chawla - Information Fusion, 2023 - Elsevier
This tutorial paper provides a general overview of symbolic regression (SR) with specific
focus on standards of interpretability. We posit that interpretable modeling, although its …
focus on standards of interpretability. We posit that interpretable modeling, although its …
[HTML][HTML] Probabilistic grammars for equation discovery
Equation discovery, also known as symbolic regression, is a type of automated modeling
that discovers scientific laws, expressed in the form of equations, from observed data and …
that discovers scientific laws, expressed in the form of equations, from observed data and …
Controllable neural symbolic regression
In symbolic regression, the objective is to find an analytical expression that accurately fits
experimental data with the minimal use of mathematical symbols such as operators …
experimental data with the minimal use of mathematical symbols such as operators …
Learning symbolic expressions: Mixed-integer formulations, cuts, and heuristics
In this paper, we consider the problem of learning a regression function without assuming its
functional form. This problem is referred to as symbolic regression. An expression tree is …
functional form. This problem is referred to as symbolic regression. An expression tree is …