Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Physics-informed machine learning

Physics-informed neural networks (PINNs) for fluid mechanics: a review

Greedy function approximation: A gradient boosting machine.

Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent …