Results 31 to 40 of about 2,349 (245)
DSFA-PINN: Deep Spectral Feature Aggregation Physics Informed Neural Network
IEEE Access, 2022 Solving parametric partial differential equations using artificial intelligence is taking the pace. It is primarily because conventional numerical solvers are computationally expensive and require significant time to converge a solution. However, physics
Muhammad Rafiq +2 more
doaj +1 more source
Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs)
Communications in Computational Physics, 2023 This paper explores the difficulties in solving partial differential equations (PDEs) using physics-informed neural networks (PINNs). PINNs use physics as a regularization term in the objective function. However, a drawback of this approach is the requirement for manual hyperparameter tuning, making it impractical in the absence of validation data or ...
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Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
CoRR, 2022 To appear in NeurIPS 2022 Workshop on The Symbiosis of Deep Learning and Differential Equations (DLDE) - II, 12 pages, 5 figures, full paper: arXiv:2306 ...
Junwoo Cho +5 more
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Physics-informed Neural Network for Nonlinear Dynamics in Fiber Optics
, 2021 A physics-informed neural network (PINN) that combines deep learning with physics is studied to solve the nonlinear Schr\"odinger equation for learning nonlinear dynamics in fiber optics.
Alan Pak Tao Lau (5683478) +5 more
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NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
, 2023 This paper presents NSGA-PINN, a multi-objective optimization framework for the effective training of physics-informed neural networks (PINNs). The proposed framework uses the non-dominated sorting genetic algorithm (NSGA-II) to enable traditional ...
Christian Moya, Binghang Lu, Guang Lin
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NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations
CoRR, 2021 Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical models. In this manuscript we detail the inner workings of NeuralPDE.jl and show how a formulation structured around numerical quadrature gives rise to new loss functions ...
Kirill Zubov +13 more
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A PHYSICS INFORMED NEURAL NETWORK (PINN) APPROACH FOR SOIL-PILE INTERACTION
Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2015), 2023 This work presents a reference solution for a typical soil-pile interaction problem, by means of a Physics Informed Neural Network (PINN). Advanced elastodynamic solutions for pile response can perform as scoping tools in early design stage and complement finite element simulations serving as “benchmark” solutions to allow the verification of more ...
Madianos, Michail +6 more
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, 2022 The velocities measured by particle image velocimetry (PIV) and particle tracking velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity field with high resolution is indispensable for data visualization and analysis.
王士召, 刘毅, 王洪平
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Sinogram-based flow estimation in computed tomography using a physics-informed neural network: Impact of gantry rotation speed, X-ray fluence and pulsed acquisition on accuracy. [PDF]
Med PhysAbstract Background Non‐invasive imaging‐based assessment of blood flow plays a critical role in evaluating heart function and structure. Computed Tomography (CT) is a widely‐used imaging modality that can robustly evaluate cardiovascular anatomy and function, but direct methods to estimate blood flow velocity from movies of contrast dynamics have not ...
Guo J +4 more
europepmc +2 more sources
, 2022 The problem of electro-thermal coupling is widely present in the integrated circuit (IC). The accuracy and efficiency of traditional solution methods, such as the finite element method (FEM), are tightly related to the quality and density of mesh ...
Yaoyao Ma +7 more
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