Inverse problems are situations where you start with the results or effects and work backward to determine the causes or inputs. For example, in medical imaging, doctors may use data from scans to infer the structure of internal organs. This process can be complex because multiple causes can lead to the same effect. These problems are common in various fields, including geophysics, engineering, and machine learning. Solving inverse problems often requires specialized mathematical techniques, as the relationship between inputs and outputs can be nonlinear and ill-posed, meaning small changes in data can lead to large variations in results.