Architecture of the modified DeepONet adopted in this work. The network learning the operator G : (E, Φ) → G(E, Φ) includes three inputs, the function, E, the parameter, Φ. E is presented as some discrete locations with m elements sampled on $[{\overline{t}}_1,{\overline{t}}_2,\mathrm{...},{\overline{t}}_m]$. In this work, $\overline{z}$ limited in a small range is considered as the only variable input to trunk net. The input of SELayer receives the Hadamard product of the outputs from branch net[ and branch net₂. The final output is the dot product of [b₁, b₂,…, b_p] and [t₁, t₂,…, t_p] with a bias.