In a finite element code using magnetic vector potential as unknown, the fixed point method needs the relationship which gives the magnetic field H from the magnetic flux density B. In this paper, we present two methods to obtain the magnetic behaviour H(B) of a ferromagnetic material. The first uses centred cycles to calculate directly the inverse Preisach model. The second uses the Regula-Falsi method and the classical direct Preisach model (where B, H, and M are collinear). We conclude giving some elements of comparison.

This paper deals with the solution of linear inverse problems in magnetostatics. The case the authors have broached is finding the current density on the basis of magnetic field values. Solving this kind of equation is an ill-posed problem. Exact magnetic field values and measured values lead to different cases, each of which is presented. To solve them, the authors use the conjugate gradient method with iterative regularization. They present numerical results for the design of magnets, gradient and shim coils, and numerical results for the problem of recovering current density values from measured field values.

Isolated cardiac cells stimulators design can be aided by numerical tools based on the behavior model. The linear model (RC circuit) is the most straightforward model for describing the electrical membrane cell behavior for an external electrical stimulation: however, an adjustment is necessary to make it competent to describe correctly the different types of excitation variability experimentally observed. A Linear Transient Model is presented, solved by the Finite Element Method.

Replication of patterns at a nanometer scale is a challenge for both advanced optical lithography and post-optical techniques. Considerable industrial effort has been devoted to the so-called leading-edge optical methods and the next generation lithography techniques. In parallel, a number of low-cost techniques such as nanoimprint and micro-contact printing are being investigated. In these methods, pattern replication is performed in non-conventional ways so that diffraction and scattering problems are no longer relevant. However, several other critical issues have to be studied. This paper describes two tri-layer pattern-transfer techniques, which can be used to improve the process latitude and the process compatibility. Pattern replication and lift-off transfer with feature sizes down to 30 nm and 150 nm have been achieved respectively by nanoimprint and micro-contact printing. As application examples, high-density magnetic dot structures are obtained and studied by measuring magneto-optical Kerr hysteresis loops.

This search deals with the control of a process in order to take into account non linearities without parameters identification. Neural networks properties are exploited for the modelling of non linear features, and a formalism is proposed to design a neural model which can be used directly as a controller. We apply this formalism to the modelling of a non linear mechanical load torque feature coupled to an induction machine in order to design a speed controller. A partial and a global neural method are presented. In order to overcome modelling errors or any process changes, an adaptive on line method is proposed. At last, simulation and experimental results are presented.

An hybrid method combining two algorithms is proposed for the global optimization of inverted pattern functions encountered in non destructive control by eddy currents. This method performs first the exploration with a Genetic Algorithm, allowing to localize a "promising area" . Then the search is intensified inside that area, through a Nelder-Mead Simplex Search.

In this paper, the stochastic magnetic field lines diffusion is studied. The stochastic behavior of the magnetic field lines is described by the generalized mapping to the second order which is derived from the magnetic field lines equations. The diffusion coefficient is calculated analytically using the Fourier paths technique and the characteristic function formalism. Both, they give exactly the same result of the diffusion coefficient. A numerical determination of the diffusion coefficient is also done.