most citedA mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method

12 citations · 12 across the 1 of their papers we have counts for

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cs.CE2022★ 12 cited

A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method

Shahed Rezaei, Ali Harandi, Ahmad Moeineddin +2

Physics-informed neural networks (PINNs) are capable of finding the solution for a given boundary value problem. We employ several ideas from the finite element method (FEM) to enh…