Post by Gabriel Kronberger

The Minisymposium for Symbolic Regression for Materials Science and Engineering took place as a part of the Materials Science and Engineering Congress on the 29th of September 2026 in Darmstadt, Germany.

The main topics were applications of symbolic regression for prediction of structure and mechanical properties of materials and crack tip location prediction. Several talks mentioned the need for handling uncertainty in data and models and using it for principled model selection to resolve overfitting in SR. Integration of physical constraints (e.g. unit-aware SR) was also mentioned by several speakers. SR implementations used: PySR (most popular), Φ-SO (convenient for physical units), and NeoGP (DL-aware model selection). SR was used with real data and data from simulations.

Post by Gabriel Kronberger

In the Minisymposium for Equation Discovery and Symbolic Regression held as part of the World Conference for Computational Mechanics (WCCM/ECCOMAS) on 20/07/2026 in Munich we had seven presentations covering algorithmic developments and applications of SR for material and fluid dynamics models.

Post by Gabriel Kronberger LinkedIn

We organized the Workshop on Symbolic Regression and Equation Discovery as a subevent within WCCI/CEC 2026 in Maastricht, Netherlands this year. After several years of hosting it at GECCO, it was a great opportunity for meeting new people, discussing SR developments, and sharing ideas.

For the first time, we used OpenReview as a platform to organize paper submissions and reviews. We received 8 submissions and accepted 5 papers, which were presented during the workshop. The accepted papers and review discussions are archived on OpenReview.

Post by Fabricio Olivetti de França (Scholar, Linkedin)

In the last post we introduced the idea of e-graphs and how it can play an important role with equation discovery (aka symbolic regression). We also introduced eggp [1], the first equation discovery algorithm that takes advantage of e-graphs by using it as a powerful database system and enforce novelty.

We also briefly introduced r🥚ression [2], a Python tool that allows us to explore the power of e-graphs in different scenario. In this post, we will play a bit more with this tool to show how powerful e-graphs can be as a go to tool for equation discovery.

For a gentle introduction to e-graphs and equality saturation, see the previous part of this blog post.

This guest post by Fabrício Olivetti de França describes the concepts of Equality Graphs and Equality Saturation and the benefits of using E-Graphs in Symbolic Regression.

This year's workshop on Symbolic Regression at GECCO (Genetic and Evolutionary Computation Conference) saw a record number of submissions and was very well received. We had two marvelous sessions with nine contributed talks and a 30-minute long lively discussion round. The overall quality of talks was high, spanning a good mix of topics including benchmarking, efficiency improvements, theoretical considerations, and applications. Thanks to all the speakers and participants for their contributions.

The meeting on Symbolic Regression in the Physical Sciences was held on 28th and 29th of April 2025 at Royal Society in London. Two days of insightful talks highlighted several applications of symbolic regression and gave some hints about future developments of symbolic regression methods. We provide our personal summary of the main topics in this post.