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.
Results
Showing 1 to 20 of 116 for “"Physics-Informed"”.
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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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Seeing Beyond Limits with Physics-Informed Priors
… refines them using deep denoisers. By embedding physics-informed priors into this optimization, it aims to surpass conventional limits in dimensionality and visibility. First, I develop Privacy Dual Imaging using an ambient light sensor. This approach tackles both dimensionality and visibility …
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Improvements on physics-informed models for lithium batteries
… a path for realising these objectives through informed control interactions, reduced degradation effects, and decreased vehicle costs. This thesis contributes to these developments through improvements in fast physics-informed battery models for both lithium-ion and lithium-metal batteries. The …
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Physics-informed data-driven frameworks for materials discovery
… for optimization and enhancement. In Chapter 3, physics-informed machine learning models are developed for the classification of printability and glass transition temperature (Tg) in additive manufacturing processes. By integrating fundamental principles of physics into the machine learning …
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Option pricing with physics-informed neutral networks (PINNS)
We investigate the application of physics-informed neural networks (PINNs) to option pricing. PINNs are neural networks that are trained to numerically solve partial differential equations (PDEs) by obeying the dynamics induced by the PDE as well as the initial/terminal conditions of the PDE. They …
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Physics-Informed Deep Learning for Plasma Etch Optimization
… improved model generalization using physically-informed neural networks (PINNs) with a level-set based loss. We highlight performance metrics for Soft-Adapt learned physics loss weights, as well as statically chosen weights. Future work includes utilizing the PINN model in a Bayesian framework …
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Physics-informed neural surrogates for next-generation aerothermochemical modeling
… this thesis proposes a modular, three-stage, physics-constrained, data-driven framework that preserves the fidelity of detailed state-specific CR kinetics while achieving the computational efficiency required for practical aerospace applications. The first stage reduces the dimensionality of …
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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 Neural Networks for Structural Health Monitoring of Bridges
… and lack physical interpretability, while purely physics-based models can be difficult to update with sparse, noisy monitoring data. This thesis investigates physics-informed neural network (PINN)-based frameworks for interpreting bridge monitoring data, with the overarching aim of enabling …
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Physics-informed neural network for damage identification in railroad bridges
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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Scalable online nonlinear goal-oriented inference with physics-informed maps
This thesis develops a physics-informed k-nearest neighbors approach, which draws from both physics-based modeling and data-driven machine learning. In doing so, our method achieves robustness and increased accuracy with small datasets, while being cheap to apply. Our method tackles the challenges …
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INTEGRATION OF PHYSICS-INFORMED APPROACHES WITH MACHINE LEARNING TECHNIQUES FOR AERODYNAMICS
… a fusion of machine learning techniques and physics laws to tackle aerodynamic challenges. Specifically, it employs machine learning methodologies to enhance various aspects of aerodynamics, including the optimization of airfoil designs, prediction of 2D flow fields, and refinement of flush …
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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 Interpretable Attention-based Machine Learning for Jet Turbine Prediction
… to the loss function to include the physics of key performance parameters of the gas turbine as residual form equations, finding that it increases model accuracy and the usefulness of interpretability results, even when model size is reduced. These key performance parameters were …
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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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