# Exploring Multi-view Symbolic Regression methods in physical sciences

arXiv: https://arxiv.org/abs/2509.10500 · HTML: https://mlsanity.com/paper/2509.10500/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients](https://mlsanity.com/paper/1912.04871/) (2019)
- [Contemporary Symbolic Regression Methods and their Relative Performance](https://mlsanity.com/paper/2107.14351/) (2021)
- [Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws](https://mlsanity.com/paper/2303.03192/) (2023)
- [Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery](https://mlsanity.com/paper/2206.10540/) (2022)
- [Fast, accurate, and transferable many-body interatomic potentials by symbolic regression](https://mlsanity.com/paper/1904.01095/) (2019)
- [Call for Action: towards the next generation of symbolic regression benchmark](https://mlsanity.com/paper/2505.03977/) (2025)
- [The Inefficiency of Genetic Programming for Symbolic Regression](https://mlsanity.com/paper/2404.17292/) (2024)
- [Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables](https://mlsanity.com/paper/2107.09484/) (2021)
- [Class Symbolic Regression: Gotta Fit 'Em All](https://mlsanity.com/paper/2312.01816/) (2023)
- [A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming](https://mlsanity.com/paper/2406.03585/) (2024)
- [Improving Genetic Programming for Symbolic Regression with Equality Graphs](https://mlsanity.com/paper/2501.17848/) (2025)
- [syren-new: Precise formulae for the linear and nonlinear matter power spectra with massive neutrinos and dynamical dark energy](https://mlsanity.com/paper/2410.14623/) (2024)
- [Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles](https://mlsanity.com/paper/2209.06454/) (2022)
- [rEGGression: an Interactive and Agnostic Tool for the Exploration of Symbolic Regression Models](https://mlsanity.com/paper/2501.17859/) (2025)
- [Handbook of Network Analysis [KONECT -- the Koblenz Network Collection]](https://mlsanity.com/paper/1402.5500/) (2014)

## Abstract (arXiv)

Describing the world behavior through mathematical functions help scientists to achieve a better understanding of the inner mechanisms of different phenomena. Traditionally, this is done by deriving new equations from first principles and careful observations. A modern alternative is to automate part of this process with symbolic regression (SR). The SR algorithms search for a function that adequately fits the observed data while trying to enforce sparsity, in the hopes of generating an interpretable equation. A particularly interesting extension to these algorithms is the Multi-view Symbolic Regression (MvSR). It searches for a parametric function capable of describing multiple datasets generated by the same phenomena, which helps to mitigate the common problems of overfitting and data scarcity. Recently, multiple implementations added support to MvSR with small differences between them. In this paper, we test and compare MvSR as supported in Operon, PySR, phy-SO, and eggp, in different real-world datasets. We show that they all often achieve good accuracy while proposing solutions with only few free parameters. However, we find that certain features enable a more frequent generation of better models. We conclude by providing guidelines for future MvSR developments.