1,183 research outputs found
Variable-fidelity electromagnetic simulations and co-kriging for accurate modeling of antennas
Accurate and fast models are indispensable in contemporary antenna design. In this paper, we describe the low-cost antenna modeling methodology involving variable-fidelity electromagnetic (EM) simulations and co-Kriging. Our approach exploits sparsely sampled accurate (high-fidelity) EM data as well as densely sampled coarse-discretization (low-fidelity) EM simulations that are accommodated into one model using the co-Kriging technique. By using coarse-discretization simulations, the computational cost of creating the antenna model is greatly reduced compared to conventional approaches, where high-fidelity simulations are directly used to set up the model. At the same time, the modeling accuracy is not compromised. The proposed technique is demonstrated using three examples of antenna structures. Comparisons with conventional modeling based on high-fidelity data approximation, as well as applications for antenna design, are also discussed
Identification of quasi-optimal regions in the design space using surrogate modeling
The use of Surrogate Based Optimization (SBO) is widely spread in engineering design to find optimal performance characteristics of expensive simulations (forward analysis: from input to optimal output). However, often the practitioner knows a priori the desired performance and is interested in finding the associated input parameters (reverse analysis: from desired output to input). A popular method to solve such reverse (inverse) problems is to minimize the error between the simulated performance and the desired goal. However, there might be multiple quasi-optimal solutions to the problem. In this paper, the authors propose a novel method to efficiently solve inverse problems and to sample Quasi-Optimal Regions (QORs) in the input (design) space more densely. The development of this technique, based on the probability of improvement criterion and kriging models, is driven by a real-life problem from bio-mechanics, i.e., determining the elasticity of the (rabbit) tympanic membrane, a membrane that converts acoustic sound wave into vibrations of the middle ear ossicular bones
Adaptive active subspace-based metamodeling for high-dimensional reliability analysis
To address the challenges of reliability analysis in high-dimensional
probability spaces, this paper proposes a new metamodeling method that couples
active subspace, heteroscedastic Gaussian process, and active learning. The
active subspace is leveraged to identify low-dimensional salient features of a
high-dimensional computational model. A surrogate computational model is built
in the low-dimensional feature space by a heteroscedastic Gaussian process.
Active learning adaptively guides the surrogate model training toward the
critical region that significantly contributes to the failure probability. A
critical trait of the proposed method is that the three main ingredients-active
subspace, heteroscedastic Gaussian process, and active learning-are coupled to
adaptively optimize the feature space mapping in conjunction with the surrogate
modeling. This coupling empowers the proposed method to accurately solve
nontrivial high-dimensional reliability problems via low-dimensional surrogate
modeling. Finally, numerical examples of a high-dimensional nonlinear function
and structural engineering applications are investigated to verify the
performance of the proposed method
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