Introduction In recent years, machine learning has become widely used in scientific contexts, producing strong predictive results. However, an increasingly clear limitation has emerged: many models perform well numerically but remain difficult to interpret. At ActarusLab, our work focuses on this gap. We explore whether it is possible, starting https://scientificmachinelearning15936.liberty-blog.com/41267056/symbolic-regression-in-scientific-machine-learning-from-data-noise-to-governing-equations