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 20 for “"Scientific Machine Learning"”.
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Uncovering Perovskite Degradation Equations Using Scientific Machine Learning
… methods to infer root causes. Traditionally, machine learning (ML) applied to materials research has focused on optimization and regression over a limited training set. Inferring physical laws directly from data may allow the extraction of more generalizable scientific information that enables …
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From efficient high-order methods to scientific machine learning.
… and developing a wrapper that integrates Machine Learning with FEM packages. First, we focus on implicit Runge-Kutta methods which possess high-order accuracy and important stability properties. Implementation difficulties and the high expense of solving the coupled algebraic system at …
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From efficient high-order methods to scientific machine learning.
… and developing a wrapper that integrates Machine Learning with FEM packages. First, we focus on implicit Runge-Kutta methods which possess high-order accuracy and important stability properties. Implementation difficulties and the high expense of solving the coupled algebraic system at …
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Inertial Navigation System Drift Reduction Using Scientific Machine Learning
… accumulate over time. This thesis introduces Scientific Machine Learning (SciML) as an innovative approach to mitigate INS drift by integrating physical models with machine learning algorithms. The proposed SciML architecture leverages neural networks to learn complex error patterns and …
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SCIENTIFIC MACHINE LEARNING METHODS FOR REACTIVE-TRANSPORT AND THERMAL-TRANSPORT PROBLEMS
Scientific machine learning (SciML) involves development of machine learning models trained using scientific data. SciML involves confluence of machine learning and scientific computing tools and has accelerated research in a gamut of scientific disciplines. This dissertation presents novel SciML …
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Scientific Machine Learning for Dynamical Systems: Theory and Applications to Fluid Flow and Ocean Ecosystem Modeling
… these challenges, we develop and apply novel scientific machine learning methods to learn unknown and discover missing dynamics in models of dynamical systems. In our Bayesian approach, we develop an innovative stochastic partial differential equation (PDE) - based model learning theory and …
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Physics-informed data-driven frameworks for materials discovery
… presents 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. …
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A new way to do epidemic modeling
… for epidemic modeling, which is rooted in Scientific Machine Learning. Scientific Machine Learning (SciML) leverages the interpretability of ODEs with the expressivity of neural networks. We thus aim to retain the interpretability of compartment models along with the complexity of agent …
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Generative and Discriminative Models in Phase Transition Prediction
… parallel computation and benefit from its robust scientific machine learning ecosystem. The evaluation will focus on their performance concerning error rates, computation time, and required data. The goal is to guide researchers in selecting the optimal method within data and computational …
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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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Machine Learning for Phonon Thermal Transport
… techniques. This thesis demonstrates how machine learning can address the challenges by 1) predicting phonon DOS from simple information of atomic structures and symmetry-aware neural networks and 2) extracting frequency-based phonon information from time-resolved diffraction measurements …
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Dynamic Modeling and Real-time Simulation of Power Electronics-dominated Power Grids Using Hybrid DDM and EDDM Techniques
… decomposition phase to isolate synchronous machines and renewable sources, improving the management of these components. Each subsystem is further divided into subdomains, with separate computations performed using the Schur-complement technique. Simulations on test systems with up to 25,000 …
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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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Language Evolution for Parallel and Scientific Computing
… of techniques like sensitivity analysis and scientific machine learning. With the right methodology, these abstractions can compose with each other and specialize to the domain. I will demonstrate that the combination of high-level array-based abstractions and a lowlevel performance portable …
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High-speed high-fidelity computational modeling approaches for cardiac applications
… mechanics. The novelty of our model lies in 1) learning the underlying physics directly from the weak form of the PDE, either through the potential energy or the virtual work formulation, 2) no reliance on any additional experimental or simulation generated data for accurate predictions, 3) …
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Acceleration of combustion computation fluid dynamics simulations through machine learning
… for chemical reactions with cost-effective machine learning inference. Moreover, the proposed framework offered a practical and scalable method for combustion CFD simulations. The trained DeepONet models are integrated into an open-source CFD framework, OpenFOAM, using LibTorch, replacing …
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Novel Rheometric Techniques and Constitutive Models for Linear and Nonlinear Rheology: Applications to Polymeric Solutions and Colloidal Gels
… age. In the final section of Part I we turn to scientific machine learning techniques, informed by existing rheophysical laws, to formulate a universal differential equation to describe the thixotropic and yielding behavior response of time-evolving complex fluids. We demonstrate using …
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Moisture transport in cementitious materials via x-ray radiography and PINN
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01
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Cross-Laminated Timber made of Unmodified and Thermally Modified Yellow-Poplar Lumber
… by developing a physics-informed data-driven machine learning (PIDDML) model to capture the interplay between diffusion, adhesive layers, and moisture-induced strain. Traditional Fick's law models fail to account for these effects, resulting in large prediction errors. The PIDDML model, …
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Statistical problems with deterministic reinforcement learning and small sample biases
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms