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 21 for “"PINN"”.
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Towards Enhanced Proposals for PINN-Based Neural Sampler Training
Sampling from distributions whose density is known up to a normalizing constant is an important problem with a wide range of applications including Bayesian posterior inference, statistical physics, and structural biology. Annealing-based neural samplers seek to amortize sampling from unnormalized …
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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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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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Option pricing with physics-informed neutral networks (PINNS)
… 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 are mesh-free to …
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Physics-Informed Neural Networks (PINNS) and Inverse PINNS for the Modeling and Parameter Estimation of Electric Pumps in Liquid-Propellant Rocket Engines
… by utilizing Physics-Informed Neural Networks (PINNs) and inverse Physics-Informed Neural Networks (iPINNs). In an extreme system such as a liquidpropellent rocket engine, accurate control and efciency is imperative for successful performance. The electric pump system is inherently nonlinear and …
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Physics-Informed Neural Networks for Structural Health Monitoring of Bridges
… investigates physics-informed neural network (PINN)-based frameworks for interpreting bridge monitoring data, with the overarching aim of enabling physically consistent prediction of structural response and identification of key stiffness parameters using limited measurements. As PINNs are a …
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Computational and Machine Learning-Reinforced Modeling and Design of Materials under Uncertainty
… 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 deformation processes of Copper and a Titanium-Aluminum alloy.
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Physics-Informed Deep Learning for Plasma Etch Optimization
… 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 to facilitate recipe …
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Physics-Informed Deep Learning for Pilot Parameter Estimation and Pilot-Induced Oscillation Characterization
… employs Physics Informed Neural Networks (PINN) for the purpose of pilot parameter estimation and pilot control estimation. With much of the research utilizing model-based approaches towards this problem that experience sensitivity in the estimation, this method seeks to investigate the …
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Integrating Machine Learning Into Process-Based Modeling to Predict Ammonia Losses From Stored Liquid Dairy Manure
… 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 storage. This method involves inverse …
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Experimental Validation of Indirect Liquid Cooling Computational Thermal Modeling
… set for a physics-informed neural net- work (PINN) model by Aniruddha Bora. The PINNs simulations were shown to improve in consistency and accuracy when trained on data gathered in the experimental trials. The collected experimental data set is available to the public for training and testing …
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Chemically driven soft bioinspired systems and the variational formulation of physics-informed neural networks
… formulation, physics-informed neural networks (PINN) solve differential equations by minimizing a phenomenological loss function constructed based on these equations. However, higher order derivatives present in many differential equations lead to increased computational cost. Additionally, …
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Enhancing Quadruped Robot Design With Intelligent Physics-Informed Neural Network-Assisted Dynamic State Estimation and Active Spine Integration
… 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 constraints applied via Ordinary …
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Machine Learning and Deep Learning applications for the protection of nuclear fusion devices
… In addition, a Physics Informed Neural Network (PINN) model was proposed to bring thermal flow computation to GPUs for real-time implementation.
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Effect of End-Plate Tabs on Drag Reduction of a 3D Bluff Body with a Blunt Base
<p>This thesis involves the experimental testing of a bluff body with a blunt base to evaluate the effectiveness of end-plate tabs in reducing drag. The bluff body is fitted with interchangeable end plates; one plate is flush with the rest of the exterior and the other plate has small tabs …
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Physics-informed Machine Learning for Digital Twins of Metal Additive Manufacturing
… through Physics-informed Neural Networks (PINNs), the activation function in NNs is traditionally not designed to handle multi-scale PDEs. This work proposes a novel activation function Self-scalable tanh (Stan) function for PINNs. The proposed activation function modifies the traditional …
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Efficient modeling and waveform inversion of multicomponent seismic data for anisotropic media
… equation using physics-informed neural networks (PINNs). The physical laws governing the partial differential equations (PDEs) are used as regularization terms in the loss function. The initial conditions are enforced in a hard manner instead of including them as an additional regularization term. …
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Nondestructive evaluation of material properties using physics-informed neural networks and mechanical waves
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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Deep Learning Approaches for PDE-based Image Analysis and Beyond: From the Total Variation Flow to Medieval Paper Analysis
… So-called physics-informed neural networks (PINNs) and their variants have shown to be able to successfully approximate a large range of PDEs. However, before the advent of deep learning, many classical numerical methods had been developed to approximate PDE solutions on a discrete level. The …
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Online Aircraft System Identification Using a Novel Parameter Informed Reinforcement Learning Method
<p>This thesis presents the development and analysis of a novel method for training reinforcement learning neural networks for online aircraft system identification of multiple similar linear systems, such as all fixed wing aircraft. This approach, termed Parameter Informed Reinforcement Learning …
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