Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning

• URL: http://arxiv.org/abs/2105.02813v1
• Date: Tue, 30 Mar 2021 01:05:14 GMT
• Title: Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning
• Authors: Subhayan De, Bhuiyan Shameem Mahmood Ebna Hai, Alireza Doostan, Markus Bause
• Abstract summary: We advance existing research by accounting for uncertainty in the material and geometric properties of a structure. We develop an efficient algorithm that addresses the inherent complexity of solving the multiphysics problem under uncertainty. The proposed approach provides an accurate prediction for the WpFSI problem in the presence of uncertainty.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structural health monitoring (SHM) systems use the non-destructive testing principle for damage identification. As part of SHM, the propagation of ultrasonic guided waves (UGWs) is tracked and analyzed for the changes in the associated wave pattern. These changes help identify the location of a structural damage, if any. We advance existing research by accounting for uncertainty in the material and geometric properties of a structure. The physics model used in this study comprises of a monolithically coupled system of acoustic and elastic wave equations, known as the wave propagation in fluid-solid and their interface (WpFSI) problem. As the UGWs propagate in the solid, fluid, and their interface, the wave signal displacement measurements are contrasted against the benchmark pattern. For the numerical solution, we develop an efficient algorithm that successfully addresses the inherent complexity of solving the multiphysics problem under uncertainty. We present a procedure that uses Gaussian process regression and convolutional neural network for predicting the UGW propagation in a solid-fluid and their interface under uncertainty. First, a set of training images for different realizations of the uncertain parameters of the inclusion inside the structure is generated using a monolithically-coupled system of acoustic and elastic wave equations. Next, Gaussian processes trained with these images are used for predicting the propagated wave with convolutional neural networks for further enhancement to produce high-quality images of the wave patterns for new realizations of the uncertainty. The results indicate that the proposed approach provides an accurate prediction for the WpFSI problem in the presence of uncertainty.

Related papers

• WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration [68.25711405944239]
  Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. We introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales.
  arXiv  Detail & Related papers  (2024-07-18T11:51:01Z)
• Stochastic full waveform inversion with deep generative prior for uncertainty quantification [0.0]
  Full Waveform Inversion (FWI) involves solving a nonlinear and often non-unique inverse problem. FWI presents challenges such as local minima trapping and inadequate handling of inherent uncertainties. We propose leveraging deep generative models as the prior distribution of geophysical parameters for Bayesian inversion.
  arXiv  Detail & Related papers  (2024-06-07T11:44:50Z)
• Data assimilation and parameter identification for water waves using the nonlinear Schr\"{o}dinger equation and physics-informed neural networks [0.3495246564946556]
  The measurement of deep water gravity wave elevations using in-situ devices, such as wave gauges, typically yields sparse data. This sparsity arises from the deployment of a limited number of gauges due to their installation effort and high operational costs. We propose the application of physics-informed neural network (PINN) to reconstruct physically consistent wave fields.
  arXiv  Detail & Related papers  (2024-01-08T07:35:48Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Variational waveguide QED simulators [58.720142291102135]
  Waveguide QED simulators are made by quantum emitters interacting with one-dimensional photonic band-gap materials. Here, we demonstrate how these interactions can be a resource to develop more efficient variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-02-03T18:55:08Z)
• Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks [72.09574528342732]
  Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs) They often fail to converge to desirable solutions when the target function contains high-frequency features, due to a phenomenon known as spectral bias. In the present work, we exploit neural tangent kernels (NTKs) to investigate the training dynamics of PINNs evolving under gradient descent with momentum (SGDM)
  arXiv  Detail & Related papers  (2022-06-29T19:03:10Z)
• Detection of magnetohydrodynamic waves by using machine learning [4.680040836136128]
  Identification of different types of MHD waves is an important and challenging task in such complex wave patterns. We present two MHD wave detection methods based on a convolutional neural network (CNN) which enables the classification of waves and identification of their locations.
  arXiv  Detail & Related papers  (2022-06-15T07:35:27Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation [1.7205106391379026]
  We apply a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium. We show that the FNO network generated comparable simulations with small errors and was several orders of magnitude faster.
  arXiv  Detail & Related papers  (2021-08-20T21:09:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.