A system for retrieving performance information on algorithms for solving linear equations is proposed and a method of evaluating numerical algorithms using data analysis is described. The system is web-based with an interface composed of selection menus. Users are able to easily compare the performance of numerical algorithms by selecting a coefficient matrix and candidate algorithms.

A system for retrieving performance information on algorithms for solving linear equations is proposed and a method of evaluating numerical algorithms using data analysis is described. The system is web-based with an interface composed of selection menus. Users are able to easily compare the performance of numerical algorithms by selecting a coefficient matrix and candidate algorithms.

This paper presents a numerical solution to the shape optimization problems of steady-state viscous flow fields. The minimization problem of total dissipation energy was formulated in the domain of viscous flow fields. The shape gradient of the shape optimization problem was derived theoretically using the adjoint variable method, the Lagrange multiplier method and the formulae of the material derivative. Reshaping was carried out by the traction method proposed by one of the authors as an approach to solving domain optimization problems. The validity of the proposed method was confirmed by results of 2D and 3D numerical analyses.

Structure identification of chemical substances from infrared spectra can be done with various approaches: a theoretical method using quantum chemistry calculations, an inductive method using standard spectral databases of known chemical substances, and an empirical method using rules between spectra and structures. For various reasons, it is difficult to definitively identify structures with these methods. The relationship between structures and infrared spectra is complicated and nonlinear, and for problems with such nonlinear relationships, neural networks are the most powerful tools. In this study, we have evaluated the performance of a neural network system that mimics the methods used by specialists to identify chemical structures from infrared spectra. Neural networks for identifying over 100 functional groups have been trained by using over 10000 infrared spectral data compiled in the integrated spectral database system (SDBS) constructed in our laboratory. Network structures and training methods have been optimized for a wide range of conditions. It has been demonstrated that with neural networks, various types of functional groups can be identified, but only with an average accuracy of about 80%. The reason that 100% identification accuracy has not been achieved is discussed.