PINN
A deep learning method that embeds physical laws, such as partial differential equations, directly into the neural network loss function. By constraining the model to satisfy governing physical equations, PINNs can address both forward and inverse problems in scientific computing.
Timeline (last 90 days)
The fundamental idea of PINNs is to incorporate the residual of partial differential equations (PDEs) as an additional loss term in the training objective, so that the network satisfies the equation constraints while fitting observational data.
Methods such as neural operators, physics-informed neural networks (PINNs), and Fourier neural operators (FNOs) have made significant progress in PDE solving.
All Facts (3)
The fundamental idea of PINNs is to incorporate the residual of partial differential equations (PDEs) as an additional loss term in the training objective, so that the network satisfies the equation constraints while fitting observational data.
50%UnverifiedMethods such as neural operators, physics-informed neural networks (PINNs), and Fourier neural operators (FNOs) have made significant progress in PDE solving.
50%UnverifiedPhysics-Informed Neural Networks（PINN）由 Raissi 等人于2019年在 Journal of Computational Physics 上提出
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