2024
DOI: 10.1063/5.0226232
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Physics-informed quantum neural network for solving forward and inverse problems of partial differential equations

Y. Xiao,
L. M. Yang,
C. Shu
et al.

Abstract: Recently, physics-informed neural networks (PINNs) have aroused an upsurge in the field of scientific computing including solving partial differential equations (PDEs), which convert the task of solving PDEs into an optimization challenge by adopting governing equations and definite conditions or observation data as loss functions. Essentially, the underlying logic of PINNs is based on the universal approximation and differentiability properties of classical neural networks (NNs). Recent research has revealed … Show more

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