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 “"Operator Learning"”.
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Operator learning in the overparameterized regime
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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Continuous Representations in Machine Learning - With applications to medical imaging and operator learning
Machine learning has led to significant advancements in many areas of our everyday lives, in addition to various fields in science. Machine and particularly deep learning methods have been used for applications such as protein structure prediction, drug discovery, climate modelling and medical …
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Machine Learning in Function Spaces
Operator learning is an emerging area of machine learning which aims to learn mappings (operators) between functions from data. Many physical systems can be mathematically formulated as giving a relationship between functional data, hence operator learning has the potential to be a transformative …
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Learning Compositional Abstract Models Incrementally for Efficient Bilevel Task and Motion Planning
… In this thesis, we propose an algorithm for learning predicates from demonstrations, eliminating the need for manually specified state abstractions. Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective. …
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Human factors engineering in sonar visual displays
… of an ROV. The effects of the ROV simulation and operator learning curves are removed to compare performance changes due to the various enhancements directly. Operator comments during and after testing as well as test monitor/author observations provide insight into the experiment Test result …
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Acceleration of combustion computation fluid dynamics simulations through machine learning
… fluid dynamics simulations through Deep Operator Networks (DeepONets), achieving up to an 18 times speedup in the chemical source term evaluation. Another numerical experiment showed an overall reduction of approximately 30% in computation time. This was achieved by directly replacing the …
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Deep Learning For Surrogate Modeling And Uncertainty Quantification In Science & Engineering
Scientific machine learning (SciML) has become 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 …
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Application of data-driven neural networks to bio-inspired lattice design and prediction of multiphysics solution fields
… conventional deep neural networks and deep operator-learning surrogates that predict full temperature and stress fields from process and geometry inputs, accelerating evaluation by orders of magnitude relative to finite element analysis (FEA). Bio-inspired, low-porosity lattices take design …
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Advancing generative AI for enhanced analytics in urban and environmental monitoring
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01