Inverse problem solving involves determining the causal factors or system parameters from observed outcomes, often requiring the solution of ill-posed problems where data may be incomplete or noisy. This approach is crucial in fields like medical imaging, geophysics, and machine learning, where understanding underlying processes from indirect measurements is essential.