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Full-Text Articles in Engineering
Non-Intrusive Reduced Order Model Formulation For Inverse Shape Design Including Deforming Meshes And Multiphysics Problems., Kapil Aryal
Mechanical and Aerospace Engineering Dissertations
Despite significant advancements in computer capabilities for numerical simulations, engineers continue to face limitations when dealing with large-scale full-order model(FOM) simulations. These simulations often necessitate repeated solves, such as those encountered in inverse design, real-time solution prediction, error quantification, and solver convergence, among others. To address these challenges, reduced order modeling (ROM) has emerged as a valuable approach. This thesis focuses on the development of an ROM framework that combines Proper Orthogonal Decomposition (POD) with machine learning techniques. This integrated approach is applied to a diverse range of heat transfer and fluid flow inverse design problems. POD constructs optimal sets …
Exploring Machine Learning Techniques For Embedded Hardware, Neel R. Vora
Exploring Machine Learning Techniques For Embedded Hardware, Neel R. Vora
Computer Science and Engineering Theses
This thesis delves into the intricate symbiosis between machine learning (ML) methodologies and embedded hardware systems, with a primary focus on augmenting efficiency and real-time processing capabilities across diverse application domains. It confronts the formidable challenge of deploying sophisticated ML algorithms on resource-constrained embedded hardware, aiming not only to optimize performance but also to minimize energy consumption. Innovative strategies are explored to tailor ML models for streamlined execution on embedded platforms, with validation conducted across various real-world application domains. Notable contributions include the development of a deep-learning framework leveraging a variational autoencoder (VAE) for compressing physiological signals from wearables while …