Researchers propose OrthoReg, an orthogonal regularization technique for hybrid symbolic-neural dynamical systems. This method improves interpretability and physical consistency in modeling complex systems. By combining symbolic and neural components, builders can create more accurate and insightful models. The approach helps balance interpretability and flexibility in dynamical systems modeling.