Inverse problems are a class of mathematical problems that involve determining the input that caused a given output. In other words, they involve reconstructing the cause from the effect. This type of problem is common in various fields such as physics, engineering, geophysics, and medical imaging. Inverse problems are often ill-posed, meaning that they may not have a unique solution, or the solution may be sensitive to small changes in the input data. This makes solving inverse problems challenging, and requires the use of techniques such as regularization, optimization, and statistical methods. Inverse problems play a crucial role in areas such as image and signal processing, tomography, radar imaging, and non-destructive testing. By solving inverse problems, researchers can gain insight into the underlying processes that generate the observed data, and make valuable predictions or inferences.