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 9 of 9 for “"Neural operator"”.
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Neural Operator Models as Applied to Fluid Flow Systems and Real Ocean Dynamics
… the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid …
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Physics-informed neural surrogates for next-generation aerothermochemical modeling
… addresses numerical stiffness with two novel neural-operator-based surrogate models that replace the expensive implicit integration of kinetic systems. By embedding physical laws and governing equations directly into their architectures, these machine-learning-based surrogates achieve greater …
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Neural Operators for Learning Complex Nonlocal Mappings in Fluid Dynamics
… in machine learning and develop novel neural operator-based methods that not only possess strong representational capabilities but also preserve critical physical and mathematical principles. With the developed tools, we have demonstrated promising preliminary results in addressing …
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Deep Learning Emulators for Accessible Climate Projections
… contributes a hybrid model, called multiscale neural operator, that corrects fast low-resolution simulations by learning a hard-to-model parametrization term. This achieves to cut runtime complexity from quadratic to quasilinear which can result in a 1000x faster model on selected equations in …
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Physics-guided Machine Learning for Condition Assessment of Building Structures in Operational Environments
… (FRF) data to pre-train deep convolutional neural networks (CNNs) and fine-tune them using limited real-world measurements, significantly improving damage localisation and severity identification. Additionally, a Joint Maximum Discrepancy and Adversarial Discriminative Domain Adaptation …
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A machine learning study of wind-driven runback/flow-off multiphase flows pertinent to aircraft icing phenomena
… previous ground truth, a physics-guided Fourier neural operator is developed. Then the nonlinear dynamics of the experimental WDRWF flow are identified by utilizing an interpretable data-driven framework known as sparse identification of nonlinear dynamics. Moreover, to extract new physical …
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Machine learning methods modeling waveform, multi-parameter full waveform inversion, and uncertainty quantification
… minima. In this thesis, I propose recurrent neural network (RNN) isotropic elastic FWI. Then, I proposed the elastic implicit full waveform inversion. Instead of directly updating the elastic parameters like in the conventional FWI, I use neural networks to generate elastic models and update …
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Physics-informed Machine Learning for Digital Twins of Metal Additive Manufacturing
… data, can be handled through Physics-informed Neural Networks (PINNs), the activation function in NNs is traditionally not designed to handle multi-scale PDEs. This work proposes a novel activation function Self-scalable tanh (Stan) function for PINNs. The proposed activation function modifies …
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Continuous Representations in Machine Learning - With applications to medical imaging and operator learning
… to both continuous representations of data and neural networks. They enable resolution independent evaluation, as well as physics-informed and geometry-aware learning. In this thesis, the implications of continuous representations on open questions in machine learning research are investigated. …