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Data · dataset · 2026

Schematic of the proposed dual-network physics-informed framework

Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.6084/m9.figshare.34046271.v1

<p dir="ltr">The coordinates $(x,y)$ are provided to two neural networks that simultaneously approximate the solution $\hat{u}$ and the unknown coefficient $\hat{k}$.

Description

We used uniform, Gaussian, and exponential sampling to distribute the PDE collocation points according to the computational domain. Automatic differentiation is used to evaluate the PDE residual, while solution and coefficient data losses are combined with the PDE residual to construct the composite loss.

The two networks are jointly optimized by minimizing the resulting objective using Adam followed by L-BFGS</p>

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Catalogue records · 1

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Inferred from text
Neural networks 76%
Provenance · 3 source records, 18 field assertions
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ZivaHuboai:figshare.com:article/3404627112 d agoJSON v1
Deakin Research Onlineoai:figshare.com:article/3404627112 d agoJSON v1
DMU Figshareoai:figshare.com:article/3404627112 d agoJSON v1
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