Discovering sparse interpretable dynamics from partial observations

Identifying the governing equations of a nonlinear dynamical system is key to both understanding physical features and constructing an accurate model dynamics that generalizes well beyond available data. We propose machine learning framework for discovering these using only partial observations, combining encoder state reconstruction with sparse symbolic model. Our tests show this method can successfully reconstruct full identify underlying variety ODE PDE systems.