käänteismallinnusta

Käänteismallinnusta, known in English as inverse modeling, is a computational approach used to infer unknown parameters or properties of a system by analyzing observed data. Instead of directly simulating a system's behavior with known inputs and parameters to predict outputs, inverse modeling starts with observed outputs and seeks to find the inputs or system parameters that best explain those observations. This is often achieved by minimizing the difference between the observed data and the data predicted by a forward model, which represents the system's physics or behavior.

The process typically involves a forward model, which describes the relationship between the system's parameters and the observable data. An optimization algorithm is then used to adjust the system parameters iteratively until the forward model's predictions closely match the real-world measurements. This can be a challenging task, as inverse problems are often ill-posed, meaning they may have no solution, a unique solution, or infinitely many solutions. Techniques like regularization are frequently employed to stabilize the problem and obtain meaningful results.

Käänteismallinnusta has wide applications across various scientific and engineering disciplines. In geophysics, it is used to determine subsurface structures from seismic data. In medical imaging, it helps reconstruct images from sensor readings, such as in CT or MRI scans. It is also applied in environmental science to estimate pollutant sources from atmospheric or water measurements, and in engineering for calibrating material properties or identifying fault parameters in structures. The effectiveness of käänteismallinnusta depends heavily on the quality and quantity of the observational data and the accuracy of the forward model.