Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 70 for “"Surrogate modeling"”.
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Uncertainty-Integrated Surrogate Modeling for Complex System Optimization
<p>Approximation models such as surrogate models provide a tractable substitute to expensive physical simulations and an effective solution to the potential lack of quantitative models of system behavior. These capabilities not only enable the efficient design of complex systems, but is also …
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Surrogate modeling for large-scale black-box systems
… equations are unavailable. For such systems, surrogate models are critical for many applications, such as Monte Carlo simulations; however, existing surrogate modeling methods often are not applicable, particularly when the dimension of the input space is very high. In this research, we …
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Machine Learning based Surrogate Modeling of Electronic Devices and Circuits
L'abstract è presente nell'allegato / the abstract is in the attachment
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Multidisciplinary design optimization of dynamic systems using surrogate modeling approach
… expensive system dynamics by cheap-to-evaluate surrogate models for system derivative functions. This is advantageous to do, since it preserves the inherent nature of dynamic system to certain accuracy and enables the effi cient solution of MDO problems at the same time. The proposed method is …
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Surrogate modeling based on statistical techniques for multi-fidelity optimization
… at each step of an optimization problem. Instead surrogate models can be used to explore the design space, as they are much cheaper to evaluate. Constructing a surrogate becomes challenging when different numerical models are used to compute the same quantity, but with different levels of fidelity …
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Advanced Machine Learning for Surrogate Modeling in Complex Engineering Systems
Surrogate models are indispensable in the analysis of engineering systems. The quality of surrogate models is determined by the data quality and the model class but achieving a high standard of them is challenging in complex engineering systems. Heterogeneity, implicit constraints, and extreme …
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Deep Learning For Surrogate Modeling And Uncertainty Quantification In Science & Engineering
… an increasingly important tool for constructing surrogate models of complex physical systems, enabling rapid approximation of expensive numerical solvers and supporting tasks such as design optimization, uncertainty analysis, and autonomous experimentation. However, scientific surrogates are …
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Surrogate modeling of alternative jet fuels for study of autoignition characteristics
Recently published surrogate models are evaluated for their predictive capabilities of autoignition characteristics for alternative jet fuels. Computational simulation results are compared with published data from experimental rapid compression machine (RCM) tests for conventional jet fuel. …
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Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01
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Machine learning surrogate modeling methods in inverse high-speed link design
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01
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A hybrid global surrogate modeling software for nuclear reactor cross section estimation
… NUDGE has also been used to create a global surrogate model of a NFC simulation software (named XSgen). This model shows better performance compared to models generated by other established methods under equal constraints.
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Collision avoidance system optimization for closely spaced parallel operations through surrogate modeling
… This thesis describes the application of surrogate modeling and automated search for the purpose of tuning ACAS X for parallel operations. The performance of the tuned system is assessed using a data-driven blunder model and an operational performance model. Although collision avoidance …
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Image Segmentation, Parametric Study, and Supervised Surrogate Modeling of Image-based Computational Fluid Dynamics
… ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.
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A Graph Neural Network for pairwise surrogate modeling in population-based algorithms with tournament selection
… intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the …
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Data-driven acceleration of molecular dynamics simulation for nanoscale fluids with coarse-grained and surrogate modeling
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01
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Machine learning paradigms for building energy performance simulations
… simulations, the computation time, by using surrogate modeling, a class of supervised machine learning techniques where the output is a performance metric. Though early machine learning methods were introduced decades ago, the convergence of computation power, more data collection, and …
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Efficient time-dependent system reliability analysis
… method accounts for dependent responses from surrogate models and is therefore more accurate than existing Kriging Monte Carlo simulation methods that neglect the dependence between responses. The extension of the dependent Kriging method to systems is also a contribution of this dissertation. …
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Multi-fidelity strategies for lean burn combustor design
… fast, reliable and efficient design strategies. Surrogate modeling design strategies, including Kriging models, are currently being used to balance the challenges of accuracy and computational resource to accelerate the combustor design process. However, its feasibility still largely relies on …
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Design and optimization of computationally expensive engineering systems
… complex materials. Engineers face many modeling challenges while trying to design systems with rheological materials, pertaining to mathematical modeling and optimization. However, the use of more flexible design methods in conjunction with rheologically complex materials enhances design …
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