Japanese K-Computer, #1 very recently on the supercomputer list, data assimilation experiments for global or high-resolution weather prediction with 10.000 ensemble members are being carried out on this machine. (Science by Images)
Inverse Problems is a research area dealing with inversion of models or data. The background of inverse problems is the modelling and simulation of natural phenomena. When observations are taken of these phenomena, they are used to infer knowledge about either physical states or underlying quantities. In this case, we talk about the “inversion” of the data, calculating for example an image in computer tomography or a source reconstruction in acoustics.
For important applications dynamical processes are controlled by using measured data. Here, state estimation is cycled with propagating a reconstructed state through time with the help of a dynamical model. This area of inverse problems is called data assimilation. It is the basis for forcasting, as for example carried out in operational centres for numerical weather prediction or in hydrology.
Today, many areas of remote sensing employ inverse problems techniques. In nondestructive testing or medical imaging either active or passive instruments are used to infer knowledge about physical or biological states and processes. More and more, these techniques need to be integrated into modeling of dynamic processes, leading to strong synergy of the underlying techniques with data assimilation methods.
The goal of this wiki and community platform is to provide news, introductions and links for different areas and developments in the whole range of inverse problems and data assimilation, imaging and remote sensing.
The wiki is supervised by Roland Potthast email@example.com, see http://www.inverseproblems.info/potthast/. We are very happy to provide a login to you, when you want to contribute to the inverse problems wiki, please send us an email!