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 28 for “"Physics Informed Machine Learning"”.
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Physics-informed Machine Learning with Uncertainty Quantification
Physics Informed Machine Learning (PIML) has emerged as the forefront of research in scientific machine learning with the key motivation of systematically coupling machine learning (ML) methods with prior domain knowledge often available in the form of physics supervision. Uncertainty …
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Flexible pavement analysis using physics-informed machine learning methods
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01
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Physics-informed machine learning methods for environmental modeling and uncertainty quantification
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Developing physics-informed machine learning models for complex engineering systems design
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01
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Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning
… simulations. In this dissertation I explore two physics-informed, data-driven frameworks to improve RANS modeled Reynolds stresses. First, a Bayesian inference framework is proposed to quantify and reduce the model-form uncertainty of RANS modeled Reynolds stress by leveraging online sparse …
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Physics-informed Machine Learning for Digital Twins of Metal Additive Manufacturing
… with a better understanding of the underlying physics and includes monitoring and controlling the process in a real-world manufacturing environment. Digital Twins (DTs) are virtual representations of physical systems that enable fast and accurate decision-making. DTs rely on Artificial …
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Water Quality Control in Distribution Systems: Bayesian Optimization & Physics-Informed Machine Learning
… have traditionally been performed by means of physics-based models that involve solving complex, nonlinear systems of partial differential equations (PDEs) to simulate the underlying physical processes that govern chlorine transport and decay in the WDS. This dissertation aims to address these …
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Physics-informed machine learning for smart decision-making in ultrasonic metal welding
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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Interpretable Physics-informed Machine Learning Methods for Scientific Modeling and Data Analysis
With the recent advancement of modern machine learning methods, there are now many exciting opportunities to use machine learning in scientific research, including for modeling and data analysis. Machine learning has the potential to become an indispensable tool for scientific discovery, but it is …
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Physics-informed machine learning for the modeling and inverse design of microwave devices
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01
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Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment
… in both theory and experiment, a custom-built physics-informed deep learning framework constrained by partial differential equations is developed to accurately learn turbulent fields consistent with the two-fluid theory from partial observations of electron pressure. This calculation is not …
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Physics-Informed Machine Learning Methodologies Using RAPID for Predicting Eigenvalue and 3-D Fission Distribution in JSI TRIGA Mark-II Research Reactor
… slow and require significant computer resources. Machine learning (ML) enables computers the ability to learn from data, allowing well-trained models to produce results quickly and accurately. However, the challenges of machine learning involve proper algorithm selection and, more importantly, the …
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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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TOWARDS EFFICIENT LARGE-SCALE BI-LEVEL OPTIMIZATION, ALGORITHMS AND APPLICATIONS
… within the other. Recently, with the rise of machine learning, bi-level optimization has regained attention as a theoretical framework covering a wide range of machine learning problems, including hyperparameter optimization, neural architecture search, robust machine learning, meta-learning, …
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Physics-informed data-driven frameworks for materials discovery
… a comprehensive exploration of scientific machine learning methodologies applied to various aspects of material science and additive manufacturing. Chapter 2 introduces a scientific machine learning framework tailored to understand the synthesis process of flash graphene. Leveraging …
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Deep learning framework for solving geoacoustic inversion problems using normal mode theory
… characterization. This thesis presents a deep learning framework to overcome these limitations in shallow-water environments. The core approach involves training one-dimensional convolutional neural networks (1D-CNN) on large synthetic datasets generated using the KRAKEN normal mode acoustic …
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Physics-Aware Optimization and Data-Driven Methods for Low-Carbon Power Systems
… address these questions this thesis proposes two physics-aware optimization frameworks that coordinate grid-edge resources towards meeting three goals: improving grid efficiency, ensuring grid operability, and supporting clean energy directives. First, we propose Grid-SiPhyR (Sigmoidal …
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Accelerating Practical Engineering Design Optimization with Computational Graph Transformations
… into a code transformation framework through physics-informed machine learning surrogates, demonstrated with an airfoil aerodynamics analysis case study. Finally, it shows how a code transformations paradigm can simplify the formulation of other optimization-related aircraft development tasks …
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Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations
… can be summarized as follows: First, a physics-based, data-driven Bayesian framework is developed for estimating and reducing model-form uncertainties in RANS simulations. An iterative ensemble Kalman method is employed to assimilate sparse on-line measurement data and empirical prior …
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