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Advances in Continuous and Discrete Models

Theory and Modern Applications

Table 1 Error estimates between the exact and regularized solutions for \(\tau = 1.6\), \(\alpha \in \{0.65, 0.75, 0.85, 0.95\}\)

From: Identification of source term for the ill-posed Rayleigh–Stokes problem by Tikhonov regularization method

  ϵ
  0.1 0.01 0.001
  α = 0.65
\(\mathit{Err}^{\beta _{\mathrm{pri}}}\) 0.067015108159255 0.047468902109316 0.041441591914833
\(\mathit{Err}^{\beta _{\mathrm{pos}}}\) 0.098997404519191 0.044495631182970 0.040535488144887
  α = 0.75
\(\mathit{Err}^{\beta _{\mathrm{pri}}}\) 0.084761583230752 0.028602688042509 0.024458932308338
\(\mathit{Err}^{\beta _{\mathrm{pos}}}\) 0.053156432449751 0.028231628315379 0.024378712621981
  α = 0.85
\(\mathit{Err}^{\beta _{\mathrm{pri}}}\) 0.130209694916768 0.021179319121018 0.015465349519243
\(\mathit{Err}^{\beta _{\mathrm{pos}}}\) 0.122475577340357 0.024601414585993 0.015239026338959
  α = 0.95
\(\mathit{Err}^{\beta _{\mathrm{pri}}}\) 0.037276991722023 0.010256764619097 0.008742004875893
\(\mathit{Err}^{\beta _{\mathrm{pos}}}\) 0.134846446943958 0.010076794618172 0.009621545639896
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