We propose a neural network-based algorithm for solving forward and inverse problems for partial differential equations in unsupervised fashion. The solution is approximated by a deep neural network which is the minimizer of a cost function, and satisfies the PDE, boundary conditions, and additional regularizations. The method is mesh free and can be easily applied to an arbitrary regular domain. We focus on 2D second order elliptical system with non-constant coefficients, with application to Electrical Impedance Tomography.
In REF , the solution of an IP is parameterized by deep neural network and learned by minimizing a cost function that enforces the conditions of IP and additional regularization, where solutions to the PDE are required during the training.
Unsupervised Deep Learning Algorithm for PDE-based Forward and Inverse Problems
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Computer Science Mathematics