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 “"Physics-Guided Machine Learning"”.
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Physics Guided Machine Learning algorithm for MAX-DOAS retrieval
… inversion algorithm is incorporating the machine learning (ML) technique into the MAX-DOAS retrieval. This dissertation serves as the author's exploration of designing such an ML-based inversion algorithm. The inversion problem is formulated as a supervised learning problem and the ML …
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Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction
… geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models …
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Physics-guided Machine Learning for Condition Assessment of Building Structures in Operational Environments
… diverse operational environments. Traditional machine learning-based approaches often rely on extensive labelled datasets and assume consistent data distributions, which are impractical in real-world scenarios. Furthermore, these methods frequently lack interpretability, limiting their …
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Knowledge-guided Machine Learning for Sensor-based High-Performance Autonomous Material Characterization
Knowledge-guided machine learning enables sensor-based high-performance material characterization that drives accelerated materials discovery and manufacturing. Traditional materials discovery workflows are driven by low-throughput characterization processes that involve several manual sample …
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Science Guided Machine Learning: Incorporating Scientific Domain Knowledge for Learning Under Data Paucity and Noisy Contexts
… amount of labeled data available has helped tend machine learning (ML) research toward using purely data driven end-to-end pipelines, e.g., in deep neural network research. However, in many situations, data is limited and of poor quality. Traditional ML pipelines are known to be susceptible to …
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Achieving More with Less: Learning Generalizable Neural Networks With Less Labeled Data and Computational Overheads
Recent advancements in deep learning have demonstrated its incredible ability to learn generalizable patterns and relationships automatically from data in a number of mainstream applications. However, the generalization power of deep learning methods largely comes at the costs of working with very …