The work occupy by inverse analysis based on artificial neural network. This identification algorithm enable correct determine parameters of applied material model on creation of numerical model of construction so it's possible that the results of computerized simulation correspond with experiments. It look's like suitable approach especially in cases with complicated problems and complex models with many material parameters.

With the substantial size of the interest rate markets, the importance of accurate pricing, risk measurement and performance attribution can not be understated. However, the models used on the markets often have underlying issues with capturing the market's fundamental behavior. With this thesis, we aim to improve the pricing, risk measurement, and performance attribution of interest rate swap portfolios. The paper is divided into six main parts, by subject, to aid in achieving these goals. To begin with, we validate all cash flows with SEB to increase the validity of the results. Next, we implement an optimization-based model developed by Jörgen Blomvall to estimate multiple yield curves.  By considering innovations of the daily in-sample curves, risk factors are computed with principal component analysis. These risk factors are then used to simulate one-day and ten-day ahead scenarios for the multiple yield curves using a Monte Carlo method. Given these simulated scenarios, risk measures are then computed. When backtested, these risk measurements give an indication on the overall accuracy of the methodology, including the estimated curves, the derived risk factors, and the simulation methodology. Along with the simulation, on each out-of-sample day, monetary performance attribution for the portfolios is also performed. The performance attribution indicates what drives the value change in the portfolio. This can be used in order to evaluate the estimated yield curves and derived risk factors. The risk measurement and performance attribution is done for three different portfolios of interest rate swaps on the EUR, USD, and SEK markets. However, the risk factors are only estimated for EUR data and used for all portfolios.  The main difference to previous work in this area is that, for all implementations, a multiple yield curve environment is studied. Different PCA algorithms are evaluated to increase the precision and speed of the risk factor calculation. Mean reverting risk factors are developed in the simulation framework, along with a Latin hypercube sampling method accounting for dependence in the random variables to reduce variance. We also study the EUR and SEK markets, while the focus in previous literature is on the USD market. Lastly, we calculate and backtest the risk measures value-at-risk and expected shortfall for one-day and ten-day horizons. Four different PCA methods are implemented, a bidiagonal divide and conquer SVD algorithm, a randomized SVD method, an Arnoldi method, and an optimization-based PCA algorithm. We opt to use the first one due to high accuracy and the ability to calculate all eigenpairs. However, we recommend to use the Arnoldi method in future implementations and to further study the optimization-based method. The Latin hypercube sampling with dependence method is able to produce random variables with the same correlation as the input variables. In the simulation, we are able to produce results that pass all backtests for the risk measures considering the USD portfolio. For the EUR and SEK portfolios, it is shown that the risk measures are too conservative. The results of the mean reversion method indicate that it produces slightly less conservative estimates for the ten-day horizon. In the performance attribution, we show that we are able to produce results with small error terms, therefore indicating accurately estimated term structures, risk factors, and pricing. We conclude that we are partly able to fulfill the stated purpose of this thesis due to having produced accurate pricing and satisfactory performance attribution results for all portfolios, and stable risk measures for the USD portfolio. However, it is not possible to state with certainty that improved risk measurements have been achieved for the EUR and SEK portfolios. Although, we present several alternative approaches to remedy this in future implementations.

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The thesis presents methods and criteria for creation and optimization of design of computer experiments. Using the core of a program Freet the optimized designs were created by combination of these methods and criteria. Then, the suitability of the designs for statistical analysis of the tasks vith input random variables was assessed by comparison of the obtained results of six selected functions and the exact (analytically obtained) solutions. Basic theory, definitions of the evaluated functions, description of the setting of optimization and the discussion of the obtained results, including recommendations related to identified weaknesses of certain designs, are presented. The thesis also contains a description of an application that was created to display the results.

This thesis presents the reader the importance of optimization and probabilistic assessment of structures for civil engineering problems. Chapter 2 further investigates the combination between previously proposed optimization techniques and probabilistic assessment in the form of optimization constraints. Academic software has been developed for the purposes of demonstrating the effectiveness of the suggested methods and their statistical testing. 3th chapter summarizes the results of testing previously described optimization method (called Aimed Multilevel Sampling), including a comparison with other optimization techniques. In the final part of the thesis, described procedures have been demonstrated on the selected optimization and reliability problems. The methods described in text represents engineering approach to optimization problems and aims to introduce a simple and transparent optimization algorithm, which could serve to the practical engineering purposes.

A need to find better achievement has always been required in the space industrythrough time. Advanced technologies are provided to accomplish goals for humanityfor space explorer and space missions, to apprehend answers and widen knowledges. These are the goals of improvement, and in this thesis, is to strive and demandto understand and improve the mass of a space nozzle, utilized in an upperstage of space mission, with an expander cycle engine. The study is carried out by creating design of experiment using Latin HypercubeSampling (LHS) with a consideration to number of design and simulation expense.A surrogate model based optimization with Multidisciplinary Design Optimization(MDO) method for two different approaches, Analytical Target Cascading (ATC) and Multidisciplinary Feasible (MDF) are used for comparison and emend the conclusion. In the optimization, three different limitations are being investigated, designspace limit, industrial limit and industrial limit with tolerance. Optimized results have shown an incompatibility between two optimization approaches, ATC and MDF which are expected to be similar, but for the two limitations, design space limit and industrial limit appear to be less agreeable. The ATC formalist in this case dictates by the main objective, where the children/subproblems only focus to find a solution that satisfies the main objective and its constraint. For the MDF, the main objective function is described as a single function and solved subject to all the constraints. Furthermore, the problem is not divided into subproblems as in the ATC. Surrogate model based optimization, its solution influences by the accuracy ofthe model, and this is being investigated with another DoE. A DoE of the full factorial analysis is created and selected to study in a region near the optimal solution.In such region, the result has evidently shown to be quite accurate for almost allthe surrogate models, except for max temperature, damage and strain at the hottestregion, with the largest common impact on inner wall thickness of the space nozzle. Results of the new structure of the space nozzle have shown an improvement of mass by ≈ 50%, ≈ 15% and ≈ -4%, for the three different limitations, design spacelimit, industrial limit and industrial limit with tolerance, relative to a reference value,and ≈ 10%, ≈ 35% and ≈ 25% cheaper to manufacture accordingly to the defined producibility model.

This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.