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 18 of 18 for “"PINNs"”.
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Physics-informed neural networks (PINNs) in nonlocal elasticity [Reti neurali Informate dalla fisica (PINNs) nell’elasticità non locale]
… 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 approcci non locali stress-driven …
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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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DEVELOPMENT AND APPLICATIONS OF MACHINE/DEEP LEARNING TECHNIQUES IN FLUID DYNAMICS
… the prevailing physics-informed neural networks (PINNs) are applied to predict the fluid flows in both global and local domains. The results show better performance in PINNs than pure MLPs, and the RBF-activated PINNs are compared with the tanh-activated ones. Last, the automatic …
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Physics-Informed Neural Networks for Structural Health Monitoring of Bridges
… parameters using limited measurements. As PINNs are a relatively new development with almost no prior application to SHM at the commencement of this study, this research's approach has been incremental, starting from simple scenarios, and gradually ramping up complexity, examining both …
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Enhancing surrogate models of engineering structures with graph-based and physics-informed learning
… 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 of surrogate models used in …
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Physics-informed Machine Learning with Uncertainty Quantification
… the context of Physics-informed Neural Networks (PINNs), and develop an efficient sampling strategy to mitigate the failure modes.
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Physics-informed gated recurrent unit neural networks model for surface temperature estimation of Lithium-ion batteries
… 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, thermal, and heat generation models, …
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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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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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Experimental Validation of Indirect Liquid Cooling Computational Thermal Modeling
… 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 computational models.
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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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Deep Learning For Surrogate Modeling And Uncertainty Quantification In Science & Engineering
… a focus on physics-informed neural networks (PINNs). This setting represents a regime in which models may be trained with little or no observational data and must rely almost entirely on their ability to represent functions and their derivatives accurately in order to satisfy differential …
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SCIENTIFIC MACHINE LEARNING METHODS FOR REACTIVE-TRANSPORT AND THERMAL-TRANSPORT PROBLEMS
… utilizes physics-informed neural networks (PINNs) to solve forward and inverse problems for active cooling due to fluid circula- tion through the microvasculatures embedded in thin components. Such components are used in emerging technologies like hypersonic aircraft, space exploration …
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On Aerothermal Optimization of Low-Pressure Steam Turbine Exhaust System
… a Physical-Informed Neural Networks (PINNs)-based method to reconstruct sparse data, which has much better perfor mance than interpolation. In addition, it can be used to detect anomalies, which prevents data contamination due to mistakes in experiment. A Non-Uniform Rational B-spline …
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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 …