Regularization techniques are methods used to address ill-posed problems in inverse problems by adding additional information or constraints to stabilize the solution and prevent overfitting. These techniques play a crucial role in ensuring that small errors in the data do not lead to large deviations in the solution, enhancing the reliability and robustness of the results.
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