An Empirical Investigation of Neural ODEs and Symbolic Regression for Dynamical Systems
This work addresses the problem of modeling complex dynamical systems for scientific discovery, but it is incremental as it builds on existing methods with synthetic data.
The study investigated using Neural ODEs and Symbolic Regression to model and discover governing equations for damped oscillatory systems from noisy synthetic data, finding that Neural ODEs extrapolate effectively under certain conditions and Symbolic Regression recovers equations with some limitations.
Accurately modelling the dynamics of complex systems and discovering their governing differential equations are critical tasks for accelerating scientific discovery. Using noisy, synthetic data from two damped oscillatory systems, we explore the extrapolation capabilities of Neural Ordinary Differential Equations (NODEs) and the ability of Symbolic Regression (SR) to recover the underlying equations. Our study yields three key insights. First, we demonstrate that NODEs can extrapolate effectively to new boundary conditions, provided the resulting trajectories share dynamic similarity with the training data. Second, SR successfully recovers the equations from noisy ground-truth data, though its performance is contingent on the correct selection of input variables. Finally, we find that SR recovers two out of the three governing equations, along with a good approximation for the third, when using data generated by a NODE trained on just 10% of the full simulation. While this last finding highlights an area for future work, our results suggest that using NODEs to enrich limited data and enable symbolic regression to infer physical laws represents a promising new approach for scientific discovery.