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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 12 of 12 for “"Physics-Informed Neural Network"”.
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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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A physics-informed neural network modelling methodology to analyse integrated thermofluid systems
Physics-informed neural networks (PINNs) were developed to overcome the limitations of acquiring large training datasets that are commonly encountered when using purely data-driven machine learning methods. This study explores a PINN modelling methodology to analyse steady-state integrated …
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Enhancing Quadruped Robot Design With Intelligent Physics-Informed Neural Network-Assisted Dynamic State Estimation and Active Spine Integration
… is introduced, which uses a combination of a Physics-Informed Neural Network (PINN) and an Unscented Kalman Filter (UKF) with proprioceptive sensory data to enhance state estimation accuracy. This approach effectively calibrates the Inertial Measurement Unit (IMU), mitigates IMU drift through …
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Computational and Machine Learning-Reinforced Modeling and Design of Materials under Uncertainty
… satisfy design constraints associated with the physics of the system. Therefore, the physics-informed neural network (PINN) is developed to incorporate problem physics in the machine learning formulation. In this study, a PINN model is built and integrated into materials design to study the …
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Production Analysis in Tight/Shale Reservoirs Via Machine Learning Approaches
… by low permeability and complex fracture networks, positioning tight/shale gas as a pivotal component of the global energy mix. The accurate prediction of well production dynamics in these complex formations is a formidable challenge. Traditional empirical and numerical approaches, often …
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Machine Learning and Deep Learning applications for the protection of nuclear fusion devices
… terminated discharges from JET. Convolutional Neural Networks (CNNs) were proposed to extract the spatiotemporal characteristics from plasma temperature, density and radiation profiles. Since the CNN is a supervised algorithm, it is necessary to explicitly assign a label to the time windows of …
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A machine learning study of wind-driven runback/flow-off multiphase flows pertinent to aircraft icing phenomena
… methodologies can effectively capture intricate physics phenomena through data assimilation, rendering them a compelling substitute for conventional approaches. In the present study, a deep-learning framework ConvLSTM-AE is developed to forecast the intricate spatial-temporal progression of an …
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Variational Inference and Probabilistic Models for Parametric Partial Differential Equations
… contribution comes from the development of a physics driven deep latent variable model. The developed variational inference framework leverages a virtual observable of a physics residual to inform the learning of jointly trained forward and inverse parametric PDE emulators. We implement our …
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Analytics-Driven Cooperative Multi-Robot Additive Manufacturing: Advancing Efficiency and Quality
… fields, validated experimentally and via a physics-informed neural network. Collectively, these contributions unify robot-, process-, structure-, and property-level analytics into a coherent decision-making foundation for cooperative multi-robot AM, supporting energy-efficient, high- …
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Integrating Machine Learning Into Process-Based Modeling to Predict Ammonia Losses From Stored Liquid Dairy Manure
… data-driven machine learning algorithms and a physics-informed neural network (PINN) to predict manure temperature. Finally, a deep-learning approach that combines process-based modeling and recurrent neural networks (LSTM) was introduced to estimate ammonia loss from dairy manure during …
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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 networks (PINNs) in nonlocal elasticity [Reti neurali Informate dalla fisica (PINNs) nell’elasticità non locale]
… risoluzione di complessi problemi non locali, le Physics-Informed Neural Networks (PINNs) vengono proposte come potente strumento computazionale, dimostrando la loro efficacia nei problemi agli autovalori per travi non locali. La metodologia viene inoltre estesa a continui bidimensionali mediante …