Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

21 Sep 2018Dongkun ZhangLu LuLing GuoGeorge Em Karniadakis

Physics-informed neural networks (PINNs) have recently emerged as an alternative way of solving partial differential equations (PDEs) without the need of building elaborate grids, instead, using a straightforward implementation. In particular, in addition to the deep neural network (DNN) for the solution, a second DNN is considered that represents the residual of the PDE... (read more)

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