Preface of the guest-editor

✍ Scribed by guest-editor Rolf Mahnken

• Publisher: John Wiley and Sons
• Year: 2007
• Tongue: English
• Weight: 28 KB
• Volume: 30
• Category: Article
• ISSN: 0936-7195
• DOI: 10.1002/gamm.200790022

✦ Synopsis

The reliable simulation of a complex physical process is an important task in various disciplines such as biophysics, chemistry, geophysics, medicine and engineering. For the latter the predictive simulation of crash scenarios is a challenging example in automobile industry. All applications are driven by the common desire to obtain a satisfying prediction of the real process. To this end a mathematical model is formulated, which consists e.g. of a set of partial differential equations, thus rendering the direct problem. Then experimental data are necessary for identification of the model parameters, which enter these equations, and this task defines the associated inverse problem. In general this overdetermined problem has no solution for an exact agreement of experimental and simulated data. To overcome this ill-posed property, scientists and practitioners from various disciplines use the same idea by minimizing the distance of simulated and experimental data. The least-square function introduced by Carl Friedrich Gauss (1777-1855) at the age of 18 nowadays still is the most popular approach.