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 17 of 17 for “"physics-based simulations"”.
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Machine Learning based Predictive Modeling of Stochastic Systems
… include statistical signal processing and physics-based simulations. However, statistical signal processing methods often struggle to fully utilize complex and rich datasets, while physics-based simulations can be computationally demanding. As an alternative approach, machine learning (ML) …
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ENHANCING BUILDING FAULT DETECTION, DIAGNOSTICS, AND PROGNOSTICS THROUGH A HYBRID PHYSICS-INFORMED MODELING FRAMEWORK FOR OVERCOMING QUANTITY AND TEMPORAL DATA SCARCITY
… fault data. This thesis proposes a hybrid physics-informed modeling framework to overcome both data quantity scarcity and temporal sparsity. First, Hybrid Conditional Generative Adversarial Network (HCGAN) is developed. By leveraging physics-based simulations as conditional priors, it …
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Incorporation of Hysteretic Effects in Model-Order Reduction Analysis of Magnetic Devices
… of wide-bandwidth models requires detailed, physics-based simulations that utilize significant computational resources. Balancing the trade-offs between model computational overhead and accuracy can be cumbersome, especially when the nonlinear effects of saturation and hysteresis are included …
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Characterizing Effective System Architectures for Cislunar Space Situational Awareness
… analysis of existing literature and first-order physics-based simulations. These evaluations correlate specific design features with enhanced system suitability. Particularly beneficial are constellation proximity to targets, strategic constellation placement and phasing, sensor orbital …
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Development of Deep Site Specific and Reference Shear Wave Velocity Profiles in the Canterbury Plains, New Zealand
… analysis of future ground motions, full 3D physics based simulations, or to refine 3D velocity models for the region. </p>
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Generative Bayesian Optimization for Structured Design
… closed-form expressions and may require costly physics-based simulations or laboratory experiments to evaluate, motivating sample-efficient optimization methods. Bayesian optimization (BO) is a widely used machine learning method for sample-efficient black-box optimization. However, standard BO …
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Numerical Investigations of Geologic CO2 Sequestration Using Physics-Based and Machine Learning Modeling Strategies
… and sequestration (CCS) is an engineering-based approach for mitigating excess anthropogenic CO2 emissions. Deep brine aquifers and basalt reservoirs have shown outstanding performance in CO2 storage based on their global widespread distribution and large storage capacity. Capillary …
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Reliably arranging objects : a conformant planning approach to robot manipulation
… belief-state transition model from on-line physics-based simulations and supervised learning based on off-line physics simulations. Conformant planning through plan improvement. This approach takes a deterministic manipulation plan and augments it by adding fixtures (movable obstacles) to …
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AI for materials design: Generative AI with multi-fidelity strategies
… high-fidelity property labels through quantum or physics-based simulations. This dissertation introduces a unified framework that combines generative artificial intelligence (AI), hierarchical transfer learning, multi-fidelity modeling, and graph-driven voxel-based analysis to address four …
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Enhancing Hex-dominant Meshes: Generation, Evaluation, and Simplification
… and mesh elements of desired quality to perform physics-based simulations over complex geometries. Unfortunately, in practice, most automatic hex-dominant generation algorithms for various geometries may contain unpredictable mesh elements, low-quality components, and undesired configurations. …
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Geometric representation learning for chemical property prediction, structure elucidation, and molecular design
… is increasingly being used to replace expensive physics-based simulations and even experimental measurements of chemical properties. In generative chemistry, deep generative models are powering molecular design and optimization campaigns across chemical industries. Notably, this paradigm shift …
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Transfer learning in small-molecule drug discovery: From physics-based in-silico scores to realistic preclinical endpoints
… models on labels from public datasets or from physics-based simulations that are comparatively cheap to obtain. We find that transfer learning can greatly increase generalisation, even when downstream tasks are biologically unrelated to the original tasks. The dissertation includes one review …
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Multiphysics Transport in Heterogeneous Media: from Pore-Scale Modeling to Deep Learning
… phenomena in porous media are studied through physics-based simulations, and the effective solution of forward and inverse transport phenomena problems in heterogeneous media is tackled using data-driven, deep learning approaches. For nanoscale transport in porous media, the storage and …
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Polysaccharides in Amyloid Aggregation and Hydrogel Mechanics: Insights from Molecular Modeling
… with molecular dynamics (MD), a method of physics-based simulations of molecular motions. The conventional MD approach, also known as “atomistic” MD, where each atom of a molecule is explicitly modeled, is accurate but inefficient at sampling the timescales necessary for large dynamical …
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Physics-based and data-driven modeling of multi-active material electrode batteries
… that could be more efficiently explored through physics-based simulations, leading to a growing demand for improved battery simulation frameworks capable of accounting for parallel reactions and diffusion pathways, phase transformations, multi-scale heterogeneities, and interactions between …
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Finite Element Modeling Driven by Health Care and Aerospace Applications
… is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This …
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Scientific deep learning for efficient modeling and uncertainty quantification in engineering systems
… outcome requires a large number of these simulations, typically for a partial differential equation, which can be limited by the available computational resources. On the other hand, reliable analysis of the response of engineering systems often requires taking into account the inherent …