A finite element human body model (FE HBM) is a detailed virtual model of the human body that, for example, is used for simulating traffic accidents. A problem with HBMs is that there is no simple way to position the HBMs in non-standard positions. As different postures during an impact will affect the body in different ways it is vital to have the ability to position the HBMs. In this project it was investigated if it is possible to position a HBM from THUMS, by first positioning only the skin and skeleton, as control points, in the 3D-graphics program Blender. Thereafter a radial basis function interpolation is utilised to morph the rest of the HBM into the new position. The results indicate that in theory, it is possible to position a HBM using a 3D-graphics software. However, the method developed in this project resulted in a disfigurement of the morphed model. The disfigurement is possibly due to the change in distance between the skin and skeleton when positioning those body parts in Blender. / En finit element människokroppsmodell (FE HBM) är en detaljerad virtuell modell av människokroppen som exempelvis används för att simulera trafikolyckor. Ett problem med HBM:er är att det inte finns något enkelt sätt att positionera dem i annat än standardpositioner. Eftersom olika kroppsställningar påverkar kroppen på olika sätt under en kollision är det viktigt att ha möjlighet att kunna positionera en HBM. I detta projekt undersöktes om det är möjligt att positionera en HBM från THUMS, genom att först positionera endast huden och skelettet, som kontrollpunkter, i 3D-grafikprogrammet Blender. Därefter användes en radiell basfunktionsinterpolation för att flytta resten av HBM till den nya positionen. Resultaten indikerar att det är möjligt att positionera en HBM med hjälp av ett 3D-grafikprogram. Metoden som utvecklades i detta projekt resulterade dock i en deformering av den positionerade modellen. Deformeringen beror möjligen på att avståndet mellan hud och skelett ändrades vid positioneringen av dessa kroppsdelar i Blender.

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An Automated Method for Hot-to-Cold Geometry Mapping.

This project presents the development and implementation of a GPU-accelerated meshless two-phase incompressible fluid flow solver. The solver uses a variant of the Generalized Finite Difference Meshless Method presented by Gerace et al. [1]. The Level Set Method [2] is used for capturing the fluid interface. The Compute Unified Device Architecture (CUDA) language for general-purpose computing on the graphics-processing-unit is used to implement the GPU-accelerated portions of the solver. CUDA allows the programmer to take advantage of the massive parallelism offered by the GPU at a cost that is significantly lower than other parallel computing options. Through the combined use of GPU-acceleration and a radial-basis function (RBF) collocation meshless method, this project seeks to address the issue of speed in computational fluid dynamics. Traditional mesh-based methods require a large amount of user input in the generation and verification of a computational mesh, which is quite time consuming. The RBF meshless method seeks to rectify this issue through the use of a grid of data centers that need not meet stringent geometric requirements like those required by finite-volume and finite-element methods. Further, the use of the GPU to accelerate the method has been shown to provide a 16-fold increase in speed for the solver subroutines that have been accelerated.

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The thesis compares several non-linear regression methods on synthetic data sets gen- erated using standard benchmarks for a continuous black-box optimization. For that com- parison, we have chosen the following regression methods: radial basis function networks, Gaussian processes, support vector regression and random forests. We have also included polynomial regression which we use to explain the basic principles of regression. The com- parison of these methods is discussed in the context of black-box optimization problems where the selected methods can be applied as surrogate models. The methods are evalu- ated based on their mean-squared error and on the Kendall's rank correlation coefficient between the ordering of function values according to the model and according to the function used to generate the data. 1