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Three dimensional modelling of the peach in MAPLE

Bartoň, Stanislav (2008)

Programs and Algorithms of Numerical Mathematics

Linearized Gauss-Newton iteration method is used to determine main axes of the three-dimensional ellipsoid approximating a peach. Three independent photos displaying the peach as ground, side, and front view are used as data sources. System MAPLE 11 was used as a computer environment. A practical example is presented in order to demonstrate the usage of all required commands. The quality of approximation is evaluated as a final part of the paper.

Three-dimensional numerical model of neutron flux in hex-Z geometry

Hanuš, Milan, Berka, Tomáš, Brandner, Marek, Kužel, Roman, Matas, Aleš (2008)

Programs and Algorithms of Numerical Mathematics

We present a method for solving the equations of neutron transport with discretized energetic dependence and angular dependence approximated by the diffusion theory. We are interested in the stationary solution that characterizes neutron fluxes within the nuclear reactor core in an equilibrium state. We work with the VVER-1000 type core with hexagonal fuel assembly lattice and use a nodal method for numerical solution. The method effectively combines a whole-core coarse mesh calculation with a more...

Time-periodic solutions of a quasilinear beam equation via accelerated convergence methods

Eduard Feireisl (1988)

Aplikace matematiky

The author investigates time-periodic solutions of the quasilinear beam equation with the help of accelerated convergence methods. Using the Newton iteration scheme, the problem is approximated by a sequence of linear equations solved via the Galerkin method. The derivatiove loss inherent to this kind of problems is compensated by taking advantage of smoothing operators.

Variational Gaussian process for optimal sensor placement

Gabor Tajnafoi, Rossella Arcucci, Laetitia Mottet, Carolanne Vouriot, Miguel Molina-Solana, Christopher Pain, Yi-Ke Guo (2021)

Applications of Mathematics

Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used to provide observations. When sensor location is optimally selected, the predictive model can greatly reduce its internal errors. A greedy-selection algorithm is used for locating these optimal spatial locations from a numerical embedded space. A novel architecture for solving this big data problem is proposed, relying on a variational...

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