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 13 of 13 for “"Physics-informed neural networks (PINNs)"”.
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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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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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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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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 Machine Learning for Digital Twins of Metal Additive Manufacturing
… with a better understanding of the underlying physics and includes monitoring and controlling the process in a real-world manufacturing environment. Digital Twins (DTs) are virtual representations of physical systems that enable fast and accurate decision-making. DTs rely on Artificial …
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Efficient modeling and waveform inversion of multicomponent seismic data for anisotropic media
… for solving the acoustic wave 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 …
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Deep Learning Approaches for PDE-based Image Analysis and Beyond: From the Total Variation Flow to Medieval Paper Analysis
… part of the thesis, we present a supervised neural network approximation of the spectral TV decomposition which significantly speeds up its numerical solution. We report up to four orders of magnitude speedup in processing of mega-pixel size images, compared to classical GPU implementations …