Data driven structural dynamic response reconstruction using segment based generative adversarial networks

2021 ◽  
Vol 234 ◽  
pp. 111970
Author(s):  
Gao Fan ◽  
Jun Li ◽  
Hong Hao ◽  
Yu Xin
Keyword(s):  
Dynamic Response ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Structural Dynamic ◽  
Adversarial Networks ◽  
Response Reconstruction

Downscaling of surface wind speed over the North Atlantic using conditional Generative Adversarial Networks

2020 ◽  
Author(s):  
Mikhail Krinitskiy ◽  
Svyatoslav Elizarov ◽  
Alexander Gavrikov ◽  
Sergey Gulev
Keyword(s):  
North Atlantic ◽  
Surface Wind ◽  
Wrf Model ◽  
Surface Wind Speed ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Dynamic Simulations ◽  
Computational Costs ◽  
The North ◽  
Adversarial Networks

<p>Accurate simulation of the physics of the Earth`s atmosphere involves solving partial differential equations with a number of closures handling subgrid processes. In some cases, the parameterizations may approximate the physics well. However, there is always room for improvement, which is often known to be computationally expensive. Thus, at the moment, modeling of the atmosphere is a theatre for a number of compromises between the accuracy of physics representation and its computational costs.</p><p>At the same time, some of the parameterizations are naturally empirical. They can be improved further based on the data-driven approach, which may provide increased approximation quality, given the same or even lower computational costs. In this perspective, a statistical model that learns a data distribution may deliver exceptional results. Recently, Generative Adversarial Networks (GANs) were shown to be a very flexible model type for approximating distributions of hidden representations in the case of two-dimensional visual scenes, a.k.a. images. The same approach may provide an opportunity for the data-driven approximation of subgrid processes in case of atmosphere modeling.</p><p>In our study, we present a novel approach for approximating subgrid processes based on conditional GANs. As proof of concept, we present the preliminary results of the downscaling of surface wind over the ocean in North Atlantic. We explore the potential of the presented approach in terms of speedup of the downscaling procedure compared to the dynamic simulations such as WRF model runs. We also study the potential of additional regularizations applied to improve the cGAN learning procedure as well as the resulting generalization ability and accuracy.</p>

scholarly journals GANs-based PIV resolution enhancement without the need of high-resolution input

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alejandro Güemes ◽  
Carlos Sanmiguel Vila ◽  
Stefano Discetti
Keyword(s):  
High Resolution ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Turbulent Channel Flow ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Incomplete Projections ◽  
Data Driven Approach ◽  
High Resolution Images

A data-driven approach to reconstruct high-resolution flow fields is presented. The method is based on exploiting the recent advances of SRGANs (Super-Resolution Generative Adversarial Networks) to enhance the resolution of Particle Image Velocimetry (PIV). The proposed approach exploits the availability of incomplete projections on high-resolution fields using the same set of images processed by standard PIV. Such incomplete projection is made available by sparse particle-based measurements such as super-resolution particle tracking velocimetry. Consequently, in contrast to other works, the method does not need a dual set of low/high-resolution images, and can be applied directly on a single set of raw images for training and estimation. This data-enhanced particle approach is assessed employing two datasets generated from direct numerical simulations: a fluidic pinball and a turbulent channel flow. The results prove that this data-driven method is able to enhance the resolution of PIV measurements even in complex flows without the need of a separate high-resolution experiment for training.

scholarly journals A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

2021 ◽  
Vol 1 (12) ◽  
pp. 819-829
Author(s):  
Teeratorn Kadeethum ◽  
Daniel O’Malley ◽  
Jan Niklas Fuhg ◽  
Youngsoo Choi ◽  
Jonghyun Lee ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks

2021 ◽  
Author(s):  
Marcus Saraiva ◽  
Avelino Forechi ◽  
Jorcy De Oliveira Neto ◽  
Antonio DelRey ◽  
Thomas Rauber
Keyword(s):  
Waveform Inversion ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Adversarial Networks

scholarly journals On generative models as the basis for digital twins

2021 ◽  
Vol 2 ◽  
Author(s):  
George Tsialiamanis ◽  
David J. Wagg ◽  
Nikolaos Dervilis ◽  
Keith Worden
Keyword(s):  
Linear Structure ◽  
Generative Models ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Deterministic Models ◽  
Adversarial Networks ◽  
Digital Twins ◽  
Different Types ◽  
Potential Use ◽  
Model Approach

Abstract A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modeling applications. Two different types of generative models are considered here. The first is a physics-based model based on the stochastic finite element (SFE) method, which is widely used when modeling structures that have material and loading uncertainties imposed. Such models can be calibrated according to data from the structure and would be expected to outperform any other model if the modeling accurately captures the true underlying physics of the structure. The potential use of SFE models as digital mirrors is illustrated via application to a linear structure with stochastic material properties. For situations where the physical formulation of such models does not suffice, a data-driven framework is proposed, using machine learning and conditional generative adversarial networks (cGANs). The latter algorithm is used to learn the distribution of the quantity of interest in a structure with material nonlinearities and uncertainties. For the examples considered in this work, the data-driven cGANs model outperforms the physics-based approach. Finally, an example is shown where the two methods are coupled such that a hybrid model approach is demonstrated.

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