The paper demonstrates standard PINN fitting for polynomial and heat-equation problems and claims PINNs are less sensitive than finite-difference methods to the CFL stability condition.
Machine learning and deep learning
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Applications and Manipulations of Physics-Informed Neural Networks in Solving Differential Equations
The paper demonstrates standard PINN fitting for polynomial and heat-equation problems and claims PINNs are less sensitive than finite-difference methods to the CFL stability condition.