#physics-informed

An imperfect neural operator only needs to place a PINN in the right solution basin, a practical recipe for anyone solving PDEs in engineering or research where labeled data is scarce and out-of-distribution accuracy matters.

This paper provides a theoretical analysis of how differential information can improve the learning rate of kernel methods in physics-informed machine learning, which is relevant for researchers working on model optimization.