Numerical simulations, generally based on equations that describe a given model and on initial data, are being applied in an ever-expanding range of scientific disciplines to approximate processes at given points in time and space. With so-called inverse problems, this critical data is missing—researchers must reconstruct approximations of the input data or of the model underlying observable data in order to generate the desired predictions.
Click here for original story, New artificial neural network model bests MaxEnt in inverse problem example
Source: Phys.org