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In this work, this limitation is resolved by introducing ensemble-based regularization based on utilizing an ensemble of unconditional realizations of the reservoir model.
A regularization based on a viscous crack resistance that even enhances the robustness of the algorithm may easily be added.
The mollification method and projection regularization based on the Laplace and Fourier transforms are applied respectively in [4] and [5].
The empirical model designed in a nonlinear mode has three unknown constants which were optimized using Tikhonov regularization based on the dataset taken from sand units.
The paper by Long [68] proposes an Adaptation Regularization based transfer learning (ARTL) framework for scenarios of labeled source data and unlabeled target data.
A further regularization based on a priori knowledge is finally introduced to compensate for possible lack of experimental information needed for completing the identification.
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To mitigate error propagation and ill-posed problem during the process of identification, a weighted regularization approach based on the proper orthogonal decomposition (POD) was proposed, where the regularization parameter was selected by the GCV method.
This implicit hybrid method with regularization is based on the CQ method, extragradient method and gradient projection algorithm with regularization.
This implicit hybrid method with regularization is based on the CQ method, extragradient method and gradient projection algorithm (GPA) with regularization.
This implicit iterative method with regularization is based on the extragradient method, approximate proximal method and gradient projection algorithm (GPA) with regularization.
In view of the insufficient measurement, the source sparse characteristic and the oversmoothing of l2-norm regularization in BLT, linear system Eq. (4) is covert to the following l1-norm regularization problem based on the CS theory: where ‖ x ‖ 1 = ∑ i x i is the l1 norm of x.
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