DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data

Understand

In recent years, data-driven methods have been developed to learn dynamical systems and partial differential equations (PDE).

• The goal of such work is discovering unknown physics and the corresponding equations.
• However, prior to achieving this goal, major challenges remain to be resolved, including learning PDE under noisy data and limited discrete data.
• To overcome these challenges, in this work, a deep-learning based data-driven method, called DL-PDE, is developed to discover the governing PDEs of underlying physical processes.