Simulating Surface Wave Dynamics with Convolutional Networks

Lino, Mario; Cantwell, Chris D.; Fotiadis, Stathi; Pignatelli, Eduardo; Bharath, Anil A.

doi:10.48550/arxiv.2012.00718

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…The U-Net architecture was initially introduced by [31] for biomedical image segmentation and is based on a fully convolutional neural network. U-Net models were also used for steadystate and dynamic CFD applications, see [5,10], as well as for the prediction of surface waves, [25]. A modified version of the UResNet model used for the prediction of the horizontal and vertical flux components per pixel using the nearest neighbor upsampling was proposed in [36].…”

Section: Introductionmentioning

confidence: 99%

A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion

2022

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In this contribution we propose a data-driven surrogate model for the prediction of permeabilities and laminar flow through two-dimensional random micro-heterogeneous materials; here Darcy’s law is used. The philosophy of the proposed scheme is to provide a large number of training sets through a numerically “cheap” (stochastic) model instead of using an “expensive” (FEM) one. In order to achieve an efficient computational tool for the generation of the database (up to $$10^3$$ 10 3 and much more realizations), needed for the training of the neural networks, we apply a stochastic model based on the Brownian motion. An efficient algebraic algorithm compared to a classical Monte Carlo approach is based on the evaluation of stochastic transition matrices. For the encoding of the microstructure and the optimization of the surrogate model, we compare two architectures, the so-called UResNet model and the Fourier Convolutional Neural Network (FCNN). Here we analyze two FCNNs, one based on the discrete cosine transformation and one based on the complex-valued discrete Fourier transformation. Finally, we compare the flux fields and the permeabilities for independent microstructures (not used in the training set) with results from the $$\hbox {FE}^2$$ FE 2 method, a numerical homogenization scheme, in order to demonstrate the efficiency of the proposed surrogate model.

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Section: Introductionmentioning

confidence: 99%

A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion

2022

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“…This approach was used for the propagation of seismic waves in [38]. Convolutional neural networks and recurrent neural networks were used to simulate the wave dynamics in [39] and [40], respectively. For seismic wave simulation, [41] used convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

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

De,

Hai,

Doostan

et al. 2021

Preprint

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Structural health monitoring (SHM) systems use 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.

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Simulating Surface Wave Dynamics with Convolutional Networks

Cited by 2 publications

References 18 publications

A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion

A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion

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

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