On generative models as the basis for digital twins

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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Generative Adversarial Networks (GANs)

ACM Computing Surveys ◽

10.1145/3446374 ◽

2021 ◽

Vol 54 (3) ◽

pp. 1-42

Author(s):

Divya Saxena ◽

Jiannong Cao

Keyword(s):

Optimization Technique ◽

Generative Models ◽

Generative Adversarial Networks ◽

Network Architectures ◽

Research Directions ◽

Research Issues ◽

Design And Optimization ◽

Adversarial Networks ◽

Comprehensive Survey ◽

Selection Of

Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to inappropriate design of network architectre, use of objective function, and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions, and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on the broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present promising research directions in this rapidly growing field.

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Understanding of Incipient discharges in Transformer Insulation by reconstruction of Digital Twins for the discharges using Generative Adversarial Networks

10.1109/eic49891.2021.9612289 ◽

2021 ◽

Author(s):

G. D. P. Mahidhar ◽

B. Aneesh Kumar ◽

R. Sarathi ◽

N. Taylor ◽

H. Edin ◽

...

Keyword(s):

Generative Adversarial Networks ◽

Adversarial Networks ◽

Digital Twins ◽

Transformer Insulation

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Data-Driven Enhancement of Blurry Retinal Images via Generative Adversarial Networks

Lecture Notes in Computer Science - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 ◽

10.1007/978-3-030-32239-7_9 ◽

2019 ◽

pp. 75-83 ◽

Cited By ~ 4

Author(s):

He Zhao ◽

Bingyu Yang ◽

Lvchen Cao ◽

Huiqi Li

Keyword(s):

Data Driven ◽

Generative Adversarial Networks ◽

Retinal Images ◽

Adversarial Networks

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Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation

EPJ Web of Conferences ◽

10.1051/epjconf/202125103055 ◽

2021 ◽

Vol 251 ◽

pp. 03055

Author(s):

John Blue ◽

Braden Kronheim ◽

Michelle Kuchera ◽

Raghuram Ramanujan

Keyword(s):

High Energy Physics ◽

High Energy ◽

Generative Models ◽

Generative Adversarial Networks ◽

Detector Response ◽

Event Simulation ◽

Simulation Process ◽

Adversarial Networks ◽

Wide Range ◽

Detector Simulation

Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein Generative Adversarial Networks to simulate both hadronization and the detector response to jets. Our model takes the 4-momenta of jets formed from partons post-showering and pre-hadronization as inputs and predicts the 4-momenta of the corresponding reconstructed jet. Our model is trained on fully simulated tt events using the publicly available GEANT-based simulation of the CMS Collaboration. We demonstrate that the model produces accurate conditional reconstructed jet transverse momentum (pT) distributions over a wide range of pT for the input parton jet. Our model takes only a fraction of the time necessary for conventional detector simulation methods, running on a CPU in less than a millisecond per event.

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Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

IEEE Transactions on Wireless Communications ◽

10.1109/twc.2021.3103971 ◽

2021 ◽

pp. 1-1

Author(s):

Qianqian Zhang ◽

Aidin Ferdowsi ◽

Walid Saad ◽

Mehdi Bennis

Keyword(s):

Millimeter Wave ◽

Data Driven ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Millimeter Wave Communications

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

10.5194/egusphere-egu2020-13686 ◽

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

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.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.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.

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

14th International Symposium on Particle Image Velocimetry ◽

10.18409/ispiv.v1i1.160 ◽

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.

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InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5863 ◽

2020 ◽

Vol 34 (04) ◽

pp. 4377-4384

Author(s):

Ameya Joshi ◽

Minsu Cho ◽

Viraj Shah ◽

Balaji Pokuri ◽

Soumik Sarkar ◽

...

Keyword(s):

Image Data ◽

Generative Models ◽

Generative Adversarial Networks ◽

Complex Data ◽

Challenging Problem ◽

Two Phase ◽

Adversarial Networks ◽

Generative Modeling ◽

Lack Of Control ◽

Real World Problems

Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on several synthetic and real world problems: navigating manifolds of geometric shapes with desired sizes; generation of binary two-phase materials; and the (challenging) problem of generating multi-orientation polycrystalline microstructures.

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Learning Generative Adversarial Networks from Multiple Data Sources

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/391 ◽

2019 ◽

Cited By ~ 2

Author(s):

Trung Le ◽

Quan Hoang ◽

Hung Vu ◽

Tu Dinh Nguyen ◽

Hung Bui ◽

...

Keyword(s):

Generative Models ◽

Data Sources ◽

Black Hair ◽

Primary Data ◽

Generative Adversarial Networks ◽

Auxiliary Data ◽

Adversarial Networks ◽

Multiple Data ◽

Data Source ◽

Push And Pull

Generative Adversarial Networks (GANs) are a powerful class of deep generative models. In this paper, we extend GAN to the problem of generating data that are not only close to a primary data source but also required to be different from auxiliary data sources. For this problem, we enrich both GANs' formulations and applications by introducing pushing forces that thrust generated samples away from given auxiliary data sources. We term our method Push-and-Pull GAN (P2GAN). We conduct extensive experiments to demonstrate the merit of P2GAN in two applications: generating data with constraints and addressing the mode collapsing problem. We use CIFAR-10, STL-10, and ImageNet datasets and compute Fréchet Inception Distance to evaluate P2GAN's effectiveness in addressing the mode collapsing problem. The results show that P2GAN outperforms the state-of-the-art baselines. For the problem of generating data with constraints, we show that P2GAN can successfully avoid generating specific features such as black hair.

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