# An Approach to Symbolic Regression Using Feyn

arXiv: https://arxiv.org/abs/2104.05417 · HTML: https://mlsanity.com/paper/2104.05417/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [Discovering Symbolic Models from Deep Learning with Inductive Biases](https://mlsanity.com/paper/2006.11287/) (2020)
- [Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery](https://mlsanity.com/paper/1912.04825/) (2019)
- [AI Poincar'e: Machine Learning Conservation Laws from Trajectories](https://mlsanity.com/paper/2011.04698/) (2020)
- [Learning Symbolic Physics with Graph Networks](https://mlsanity.com/paper/1909.05862/) (2019)

## Built on by (most cited first, 20 of 20)

- [Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl](https://mlsanity.com/paper/2305.01582/) (2023)
- [Exhaustive Symbolic Regression](https://mlsanity.com/paper/2211.11461/) (2022)
- [AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification](https://mlsanity.com/paper/2310.01433/) (2023)
- [Call for Action: towards the next generation of symbolic regression benchmark](https://mlsanity.com/paper/2505.03977/) (2025)
- [Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology](https://mlsanity.com/paper/2504.07379/) (2025)
- [Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition](https://mlsanity.com/paper/2304.01117/) (2023)
- [A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming](https://mlsanity.com/paper/2406.03585/) (2024)
- [Machine-Guided Discovery of a Real-World Rogue Wave Model](https://mlsanity.com/paper/2311.12579/) (2023)
- [Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients](https://mlsanity.com/paper/2401.04978/) (2024)
- [(Exhaustive) Symbolic Regression and model selection by minimum description length](https://mlsanity.com/paper/2507.13033/) (2025)
- [Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health](https://mlsanity.com/paper/2306.00153/) (2023)
- [Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients](https://mlsanity.com/paper/2409.05305/) (2024)
- [Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence](https://mlsanity.com/paper/2501.18071/) (2025)
- [VaSST: Variational Inference for Symbolic Regression using Soft Symbolic Trees](https://mlsanity.com/paper/2602.23561/) (2026)
- [Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning](https://mlsanity.com/paper/2305.01206/) (2023)
- [Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses](https://mlsanity.com/paper/2501.04547/) (2025)
- [Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series](https://mlsanity.com/paper/2603.07261/) (2026)
- [Symbolic Classification-Enabled LHC Limits for BSM Global Fits](https://mlsanity.com/paper/2605.22330/) (2026)
- [Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation](https://mlsanity.com/paper/2609.24882/) (2026)
- [SymbolicArena: A Unified Infrastructure for Benchmark Distillation and Dynamic Evaluation in Symbolic Regression](https://mlsanity.com/paper/2609.35113/) (2026)

## Abstract (arXiv)

In this article we introduce the supervised machine learning tool called Feyn. The simulation engine that powers this tool is called the QLattice. The QLattice is a supervised machine learning tool inspired by Richard Feynman's path integral formulation, that explores many potential models that solves a given problem. It formulates these models as graphs that can be interpreted as mathematical equations, allowing the user to completely decide on the trade-off between interpretability, complexity and model performance. We touch briefly upon the inner workings of the QLattice, and show how to apply the python package, Feyn, to scientific problems. We show how it differs from traditional machine learning approaches, what it has in common with them, as well as some of its commonalities with symbolic regression. We describe the benefits of this approach as opposed to black box models. To illustrate this, we go through an investigative workflow using a basic data set and show how the QLattice can help you reason about the relationships between your features and do data discovery.