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 31 for “"Physics-informed Neural Networks"”.
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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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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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Chemically driven soft bioinspired systems and the variational formulation of physics-informed neural networks
… propose a new computational framework based on neural networks to solve differential equations. Differential equations are essential in developing a mathematical description of the bioinspired systems that we have studied in this work. In the conventional formulation, physics-informed neural …
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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 …
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Computational analyses and optimization of thin-walled lattice structures and the application of physics-informed neural networks in computational mechanics
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01
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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
… engines. This is accomplished 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 …
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Scientific deep learning for efficient modeling and uncertainty quantification in engineering systems
… unsupervised learning task in these tools is the physics-informed neural networks which are a new class of deep neural networks that are trained to satisfy the governing laws of physics described in the form of partial differential equations. In the second part of the dissertation, we introduce a …
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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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Physics-informed gated recurrent unit neural networks model for surface temperature estimation of Lithium-ion batteries
… a gated recurrent unit (GRU) network with physics-informed neural networks (PINNs). The GRU processes sequential data voltage, current, and ambient temperature capturing dynamic battery behavior, while the physics-informed layers embed critical physical parameters, including electrical, …
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DEVELOPMENT AND APPLICATIONS OF MACHINE/DEEP LEARNING TECHNIQUES IN FLUID DYNAMICS
… the conventional multilayer perceptron (MLP) networks are enhanced by the Gaussian radial basis function (RBF) with trainable centers and widths. Activated by Gaussian RBFs, the proposed MLP-RBF network is more accurate and efficient in nonlinear regression problems. Secondly, in view of the …
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Option pricing with physics-informed neutral networks (PINNS)
We investigate the 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 …
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Enhancing surrogate models of engineering structures with graph-based and physics-informed learning
… this work explores the emerging technology of physics-informed neural networks (PINNs) for structural surrogate modeling, proposing two new heuristics for improving the convergence and accuracy of PINNs in practice. Combined, these contributions advance the generalizability and data efficiency …
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Optimization and Machine Learning Applied to Inverse Problems in Partial Differential Equations
… models that combine learning algorithms and neural networks when the nonlinear right-hand side function is known, optimization problems for both a cubic right-hand side function with one extra unknown parameter and more general functions with multiple unknown parameters, physics-informed …
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Seismic Data Processing and Interpretation via Deep Learning
… focuses on seismic impedance inversion using physics-informed neural networks. The inputs of the neural networks are seismic amplitude, wavelet, and a low frequency model built using well logs. The output of the neural network is the seismic impedance that corresponds to the seismic trace. The …
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Deep Learning For Surrogate Modeling And Uncertainty Quantification In Science & Engineering
… uncertainty in operator learning, it introduces Neural Epistemic Operator Networks (NEON), which integrate the Epistemic Neural Network framework with neural operators for learning function-to-function mappings. This design enables scalable and well-calibrated epistemic uncertainty estimates …
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SCIENTIFIC MACHINE LEARNING METHODS FOR REACTIVE-TRANSPORT AND THERMAL-TRANSPORT PROBLEMS
… data. The framework uses convolutional neural networks for capturing spatial patterns and long short-term memory networks for forecasting temporal variations in mixing. The framework was carefully built to ensure non-negativity of the chemical species at all space-time points. The …
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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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Physics-Informed Deep Learning for Pilot Parameter Estimation and Pilot-Induced Oscillation Characterization
… to avoid loss-of-control. This research 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 …
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Drift Correcting Mulitphysics Informed Neural Network Coupled PDE Solver
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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