First principles models, derived from fundamental physical laws, have been instrumental in the development of scientific theories and technological systems. For example, the Navier-Stokes equation offers a comprehensive description of fluid flow, enabling predictions of complex behaviors in everything from blood flow [1] to weather patterns [2]. Traditionally, this pursuit has relied on the extensive expertise of domain specialists, requiring trial and error to identify features and model structures that fit the observations. In recent years, the landscape of scientific inquiry has been transformed by the availability of machine learning frameworks, such as neural networks, support vector machines, and Gaussian processes, which offer a powerful alternative for deriving predictive models [3]. These data-driven regression methods are often complex, do not typically generalize outside of the training set, and provide limited insights into the underlying physics. For instance, while these models may be trained to accurately predict the Reynolds number, they cannot capture the competitive nature between inertial and viscous forces in fluid flow. The only data-driven modeling framework that can provide insights comparable to first principles models, to the best of our knowledge, is symbolic regression (SR) [4, 5, 6].