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.

This was the largest conferences I have visited so far and it was interesting to see the interest in equation discovery methods and symbolic regression in particular in this community. For both sessions, the room for the minisymposium was packed full with more than 50 participants.

Program

The following presentations were shown in the minisymposium. The abstracts can be accessed via the WCCM website.

Using Description Length to Guide Genetic Programming Improves Symbolic Regression Solutions, G. Kronberger, D. Bartlett, H. Desmond, P. Ferreira, F. Olivetti de Franca abstract

Identifying tractable probabilistic models through uncertainty-aware symbolic regression, B. Schuscha, C. Findenig, M. Mücke, D. Scheiber abstract

On the Effect of Prior Specification in Bayesian Symbolic Regression, G. Bomarito, P. Leser, J. Pribe, G. Weber abstract

Symbolic discovery of high-dimensional tensorial governing equations from data, B. Zhang, J. Lei abstract

Data-Driven Discovery of Governing Equations in Fluid Dynamics from Molecular Simulations, T. Chen, J. Zhang abstract

Extending a Physics-based Recrystallization Model using Genetic Programming, J. Kronsteiner, S. Raaber, G. Kronberger abstract
Fidelity Compensated Physics-Informed Surrogate Modeling,

S. Shiratori, A. Shimizu, K. Shukla, Z. Wang, G. Karniadakis abstract

High-throughput training of stochastic creep and plasticity models, B. Boyce, S. Inman, A. Robertson, R. Dingreville abstract

Interpretable Physics-Informed, Data-Driven Closure of Harmonic-Balanced Navier–Stokes Equations via Symbolic Regression, K. Shukla, G. Rigas, G. Karniadakis abstract

Additionally, there were also talks covering symbolic regression in other subevents:

Symbolic Regression of RANS-Consistent Turbulence Model Corrections, A. M. Qaragoez, A. Lucantonio abstract

Symbolic Regression of Convex Dissipation Potentials for Viscoelastic Materials via a Grammar-Based Search Space, F. Califano, J. Ciambella abstract

Symbolic Regression of Control Lyapunov Functions, A. Qaragoez, R. Wisniewski, A. Lucantonio abstract

Symbolic Regression Aerodynamic Modeling for High-Speed Projectiles at High Angle of Attack Considering Physical Constraints, T. Lin, Y. Diao, G. Wang abstract

In 2027, the conference will be held in the USA again and we plan to organize again a similar minisymposium. We are looking forward to your submissions.

Minisymposium Organizers: G. Bomarito (NASA Langley Research Center, United States), G. Kronberger (University of Applied Sciences Upper Austria, Austria), J. Hochhalter (University of Utah, United States), E. Kabliman (Leibniz Institute for Materials Engineering – IWT and University of Bremen, Germany), P. Leser (NASA Langley Research Center, United States) and J. Emery (Sandia National Laboratories, United States)