Inverse problems are mathematical or computational problems where the goal is to determine the input to a system based on observed outputs. In other words, the problem involves inferring the cause from its effects. Inverse problems can be found in many fields such as physics, engineering, geophysics, medicine, and finance. Examples of inverse problems include image reconstruction from medical imaging data, parameter estimation in modeling physical systems, and determining the structure of a material from measurements of its properties. Inverse problems are often ill-posed, meaning that they may not have a unique solution or the solution may be unstable to small changes in the input data. This makes solving inverse problems challenging and often requires regularization techniques to ensure a stable and reliable solution. Various mathematical and computational methods, such as optimization, Bayesian inference, and machine learning, are employed to solve inverse problems in different disciplines.