Non-Linear Metamodeling Extensions to the Robust Parameter Design of Computer Simulations

Robust parameter design (RPD) is used to identify a systems control settings that offer a compromise between obtaining desired mean responses and minimizing the variability about those responses. Two popular combined-array strategies the response surface model (RSM) approach and the emulator approach are limited when applied to simulations. In the former case, the mean and variance models can be inadequate due to a high level of non-linearity within many simulations. In the latter case, precise mean and variance approximations are developed at the expense of extensive Monte Carlo sampling. This research combines the RSM approach\u27s efficiency with the emulator approach\u27s accuracy. Non-linear metamodeling extensions, namely through Kriging and radial basis function neural networks, are made to the RSM approach. The mean and variance of second-order Taylor series approximations of these metamodels are generated via the Multivariate Delta Method and subsequent optimization problems employing these approximations are solved. Results show that improved prediction models can be attained through the proposed approach at a reduced computational cost. Additionally, a multi-response RPD problem solving technique based on desirability functions is presented to produce a solution that is mutually robust across all responses. Lastly, quality measures are developed to provide a holistic assessment of several competing RPD strategies

Novel Adaptive Sampling Algorithm for POD-Based Non-Intrusive Reduced Order Model

The proper orthogonal decomposition (POD) based reduced-order model (ROM) has been an effective tool for flow field prediction in the engineering industry. The sample selection in the design space for POD basis construction affects the ROM performance sensitively. Adaptive sampling can significantly reduce the number of samples to achieve the required model accuracy. In this work, we propose a novel adaptive sampling algorithm, called conjunction sampling strategy, which is based on proven strategies. The conjunction sampling strategy is demonstrated on airfoil flow field prediction within the transonic regime. We demonstrate the performance of the proposed strategy by running 10 trials for each strategy for the robustness tests. Results show that the conjunction sampling strategy consistently achieves higher predictive accuracy compared with Latin hypercube sampling (LHS) and existing strategies. Specifically, under the same computational budget (40 training samples in total), the conjunction strategy reduced the L2 error by 56.7% compared with LHS. In addition, the conjunction strategy reduced the standard deviation of L2 errors by 62.1% with a 2.6% increase on the mean error compared with the best existing strategy

State-of-the-art: AI-assisted surrogate modeling and optimization for microwave filters

Microwave filters are indispensable passive devices for modern wireless communication systems. Nowadays, electromagnetic (EM) simulation-based design process is a norm for filter designs. Many EM-based design methodologies for microwave filter design have emerged in recent years to achieve efficiency, automation, and customizability. The majority of EM-based design methods exploit low-cost models (i.e., surrogates) in various forms, and artificial intelligence techniques assist the surrogate modeling and optimization processes. Focusing on surrogate-assisted microwave filter designs, this article first analyzes the characteristic of filter design based on different design objective functions. Then, the state-of-the-art filter design methodologies are reviewed, including surrogate modeling (machine learning) methods and advanced optimization algorithms. Three essential techniques in filter designs are included: 1) smart data sampling techniques; 2) advanced surrogate modeling techniques; and 3) advanced optimization methods and frameworks. To achieve success and stability, they have to be tailored or combined together to achieve the specific characteristics of the microwave filters. Finally, new emerging design applications and future trends in the filter design are discussed

Eddy current defect response analysis using sum of Gaussian methods

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics

Analysis of the Air Quality of the Basque Autonomous Community Using Spatial Interpolation

This work presents the results obtained from a spatial modeling and analysis process on pollutants measured in the air through forty-three monitoring stations located in the three provinces of the Basque Autonomous Community (Spain). The pollutants measured correspond to the set of nitrogen oxides (nitric oxide, NO; nitrogen dioxide, NO 2 ; and nitrogen oxides, NO x ) and atmospheric particulate matter with a diameter less than or equal to 10 micrometers (PM 10 ). The objective of this work was to generate a map of the pollutants that exhaustively covers the entire area of the Basque Autonomous Community using geostatistical techniques, in such a way that it serves as a basis for short and midterm environmental studies

Integration of expert knowledge into radial basis function surrogate models

A current application in a collaboration between Chalmers University of Technology and Volvo Group Trucks Technology concerns the global optimization of a complex simulation-based function describing the rolling resistance coefficient of a truck tyre. This function is crucial for the optimization of truck tyres selection considered. The need to explicitly describe and optimize this function provided the main motivation for the research presented in this article. Many optimization algorithms for simulation-based optimization problems use sample points to create a computationally simple surrogate model of the objective function. Typically, not all important characteristics of the complex function (as, e.g., non-negativity)—here referred to as expert knowledge—are automatically inherited by the surrogate model. We demonstrate the integration of several types of expert knowledge into a radial basis function interpolation. The methodology is first illustrated on a simple example function and then applied to a function describing the rolling resistance coefficient of truck tyres. Our numerical results indicate that expert knowledge can be advantageously incorporated and utilized when creating global approximations of unknown functions from sample points

Non-Gaussian Hybrid Transfer Functions: Memorizing Mine Survivability Calculations

Hybrid algorithms and models have received significant interest in recent years and are increasingly used to solve real-world problems. Different from existing methods in radial basis transfer function construction, this study proposes a novel nonlinear-weight hybrid algorithm involving the non-Gaussian type radial basis transfer functions. The speed and simplicity of the non-Gaussian type with the accuracy and simplicity of radial basis function are used to produce fast and accurate on-the-fly model for survivability of emergency mine rescue operations, that is, the survivability under all conditions is precalculated and used to train the neural network. The proposed hybrid uses genetic algorithm as a learning method which performs parameter optimization within an integrated analytic framework, to improve network efficiency. Finally, the network parameters including mean iteration, standard variation, standard deviation, convergent time, and optimized error are evaluated using the mean squared error. The results demonstrate that the hybrid model is able to reduce the computation complexity, increase the robustness and optimize its parameters. This novel hybrid model shows outstanding performance and is competitive over other existing models

Biogeography-Based Optimization of a Variable Camshaft Timing System

Automotive system optimization problems are difficult to solve with traditional optimization techniques because the optimization problems are complex, and the simulations are computationally expensive. These two characteristics motivate the use of evolutionary algorithms and meta-modeling techniques respectively. In this work, we apply biogeography-based optimization (BBO) to radial basis function (RBF)-based lookup table controls of a variable camshaft timing system for fuel economy optimization. Also, we reduce computational search effort by finding an effective parameterization of the problem, optimizing the parameters of the BBO algorithm for the problem, and estimating the cost of a portion of the candidate solutions in BBO with design and analysis of computer experiments (DACE). We find that we can improve fuel economy by 1.7 compared to the original control parameters, and we find effective, problem-specific values for BBO population size and mutation rate. Finally, we find that we can use a small number of samples to construct DACE models, and we can use these models to estimate a significant portion of the BBO candidate solutions each generation to reduce computation effort and still obtain good BBO solution

Biogeography-Based Optimization of a Variable Camshaft Timing System

Automotive system optimization problems are difficult to solve with traditional optimization techniques because the optimization problems are complex, and the simulations are computationally expensive. These two characteristics motivate the use of evolutionary algorithms and meta-modeling techniques respectively. In this work, we apply biogeography-based optimization (BBO) to radial basis function (RBF)-based lookup table controls of a variable camshaft timing system for fuel economy optimization. Also, we reduce computational search effort by finding an effective parameterization of the problem, optimizing the parameters of the BBO algorithm for the problem, and estimating the cost of a portion of the candidate solutions in BBO with design and analysis of computer experiments (DACE). We find that we can improve fuel economy by 1.7 compared to the original control parameters, and we find effective, problem-specific values for BBO population size and mutation rate. Finally, we find that we can use a small number of samples to construct DACE models, and we can use these models to estimate a significant portion of the BBO candidate solutions each generation to reduce computation effort and still obtain good BBO solution