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 6 of 6 for “"Neural Operators"”.
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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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Principled Methods for Advancing Generative Machine Learning
… higher efficiency than methods like neural radiance fields (NeRFs). Despite their remarkable success on standard benchmarks (e.g., text-to-image generation), current generative ML models often perform poorly in more challenging scenarios that violate their implicit mathematical …
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Deep Learning For Surrogate Modeling And Uncertainty Quantification In Science & Engineering
… uncertainty in operator learning, it introduces Neural Epistemic Operator Networks (NEON), which integrate the Epistemic Neural Network framework with neural operators for learning function-to-function mappings. This design enables scalable and well-calibrated epistemic uncertainty estimates …
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Encoding parameter and structural efficiency in deep learning
… which allows for deep and highly parameterized neural network architectures which can learn complex feature transformations from raw data. The high representational power of deep neural networks, however, often comes at the cost of high model complexity which refers to the high parameterization, …
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Machine Learning Approaches to Data-Driven Transition Modeling
… the proposed transition model, a convolutional neural network-based model encodes information from boundary layer profiles into integral quantities. Such automated feature extraction capability enables generalization of the proposed model to multiple instability mechanisms, even for those where …
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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 …