Noise-aware physics-informed machine learning for robust PDE discovery
Noise-aware physics-informed machine learning for robust PDE discovery
This work is concerned with discovering the governing partial differential equation (PDE) of a physical system. Existing methods have demonstrated the PDE identification from finite observations but failed to maintain satisfying results against noisy data, partly owing to suboptimal estimated derivatives and found PDE coefficients. We address the issues by …