Unsteady reduced-order model of flow over cylinders based on convolutional and deconvolutional neural network structure

2020 ◽  
Vol 32 (12) ◽  
pp. 123609
Author(s):  
Jiang-Zhou Peng ◽  
Siheng Chen ◽  
Nadine Aubry ◽  
Zhihua Chen ◽  
Wei-Tao Wu
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Neural Network Structure

Experimental Validation of Neural-Network-Based Nonlinear Reduced-Order Model for Vertical Sloshing

2022 ◽  
Author(s):  
Marco Pizzoli ◽  
Francesco Saltari ◽  
Giuliano Coppotelli ◽  
Franco Mastroddi
Keyword(s):  
Neural Network ◽  
Experimental Validation ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order

Reduced-Order Model with an Artificial Neural Network for Aerostructural Design Optimization

2013 ◽  
Vol 50 (4) ◽  
pp. 1106-1116 ◽  
Author(s):  
Kyung Hyun Park ◽  
Sang Ook Jun ◽  
Sung Min Baek ◽  
Maeng Hyo Cho ◽  
Kwan Jung Yee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Optimization ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Artificial Neural

scholarly journals Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Franck Nguyen ◽  
Selim M. Barhli ◽  
Daniel Pino Muñoz ◽  
David Ryckelynck
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Digital Image ◽  
Mechanical Test ◽  
Reduced Order Model ◽  
Error Estimator ◽  
Reduced Order Models ◽  
Order Model ◽  
Reduced Order

In this paper, computer vision enables recommending a reduced order model for fast stress prediction according to various possible loading environments. This approach is applied on a macroscopic part by using a digital image of a mechanical test. We propose a hybrid approach that simultaneously exploits a data-driven model and a physics-based model, in mechanics of materials. During a machine learning stage, a classification of possible reduced order models is obtained through a clustering of loading environments by using simulation data. The recognition of the suitable reduced order model is performed via a convolutional neural network (CNN) applied to a digital image of the mechanical test. The CNN recommend a convenient mechanical model available in a dictionary of reduced order models. The output of the convolutional neural network being a model, an error estimator, is proposed to assess the accuracy of this output. This article details simple algorithmic choices that allowed a realistic mechanical modeling via computer vision.

Data-driven reduced order model with temporal convolutional neural network

2020 ◽  
Vol 360 ◽  
pp. 112766 ◽  
Author(s):  
Pin Wu ◽  
Junwu Sun ◽  
Xuting Chang ◽  
Wenjie Zhang ◽  
Rossella Arcucci ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Reduced Order Model ◽  
Data Driven ◽  
Order Model ◽  
Reduced Order

scholarly journals An efficient Bayesian neural network surrogate algorithm for shape detection

2022 ◽  
Vol 62 ◽  
pp. C112-C127
Author(s):  
Mahadevan Ganesh ◽  
Stuart Collin Hawkins ◽  
Nino Kordzakhia ◽  
Stefanie Unicomb
Keyword(s):  
Neural Network ◽  
Light Scattering ◽  
Inverse Problems ◽  
Inverse Scattering ◽  
Obstacle Detection ◽  
Reduced Order Model ◽  
Far Field ◽  
Order Model ◽  
Reduced Order ◽  
Comput Phys

We present an efficient Bayesian algorithm for identifying the shape of an object from noisy far field data. The data is obtained by illuminating the object with one or more incident waves. Bayes' theorem provides a framework to find a posterior distribution of the parameters that determine the shape of the scatterer. We compute the distribution using the Markov Chain Monte Carlo (MCMC) method with a Gibbs sampler. The principal novelty of this work is to replace the forward far-field-ansatz wave model (in an unbounded region) in the MCMC sampling with a neural-network-based surrogate that is hundreds of times faster to evaluate. We demonstrate the accuracy and efficiency of our algorithm by constructing the distributions, medians and confidence intervals of non-convex shapes using a Gaussian random circle prior. References Y. Chen. Inverse scattering via Heisenberg’s uncertainty principle. Inv. Prob. 13 (1997), pp. 253–282. doi: 10.1088/0266-5611/13/2/005 D. Colton and R. Kress. Inverse acoustic and electromagnetic scattering theory. 4th Edition. Vol. 93. Applied Mathematical Sciences. References C112 Springer, 2019. doi: 10.1007/978-3-030-30351-8 R. DeVore, B. Hanin, and G. Petrova. Neural Network Approximation. Acta Num. 30 (2021), pp. 327–444. doi: 10.1017/S0962492921000052 M. Ganesh and S. C. Hawkins. A reduced-order-model Bayesian obstacle detection algorithm. 2018 MATRIX Annals. Ed. by J. de Gier et al. Springer, 2020, pp. 17–27. doi: 10.1007/978-3-030-38230-8_2 M. Ganesh and S. C. Hawkins. Algorithm 975: TMATROM—A T-matrix reduced order model software. ACM Trans. Math. Softw. 44.9 (2017), pp. 1–18. doi: 10.1145/3054945 M. Ganesh and S. C. Hawkins. Scattering by stochastic boundaries: hybrid low- and high-order quantification algorithms. ANZIAM J. 56 (2016), pp. C312–C338. doi: 10.21914/anziamj.v56i0.9313 M. Ganesh, S. C. Hawkins, and D. Volkov. An efficient algorithm for a class of stochastic forward and inverse Maxwell models in R3. J. Comput. Phys. 398 (2019), p. 108881. doi: 10.1016/j.jcp.2019.108881 L. Lamberg, K. Muinonen, J. Ylönen, and K. Lumme. Spectral estimation of Gaussian random circles and spheres. J. Comput. Appl. Math. 136 (2001), pp. 109–121. doi: 10.1016/S0377-0427(00)00578-1 T. Nousiainen and G. M. McFarquhar. Light scattering by quasi-spherical ice crystals. J. Atmos. Sci. 61 (2004), pp. 2229–2248. doi: 10.1175/1520-0469(2004)061<2229:LSBQIC>2.0.CO;2 A. Palafox, M. A. Capistrán, and J. A. Christen. Point cloud-based scatterer approximation and affine invariant sampling in the inverse scattering problem. Math. Meth. Appl. Sci. 40 (2017), pp. 3393–3403. doi: 10.1002/mma.4056 M. Raissi, P. Perdikaris, and G. E. Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378 (2019), pp. 686–707. doi: 10.1016/j.jcp.2018.10.045 A. C. Stuart. Inverse problems: A Bayesian perspective. Acta Numer. 19 (2010), pp. 451–559. doi: 10.1017/S0962492910000061 B. Veihelmann, T. Nousiainen, M. Kahnert, and W. J. van der Zande. Light scattering by small feldspar particles simulated using the Gaussian random sphere geometry. J. Quant. Spectro. Rad. Trans. 100 (2006), pp. 393–405. doi: 10.1016/j.jqsrt.2005.11.053

scholarly journals Nonlinear reduced-order model for vertical sloshing by employing neural networks

2021 ◽  
Author(s):  
Marco Pizzoli ◽  
Francesco Saltari ◽  
Franco Mastroddi ◽  
Jon Martinez-Carrascal ◽  
Leo M. González-Gutiérrez
Keyword(s):  
Neural Network ◽  
Fluid Structure Interaction ◽  
Oscillation Amplitude ◽  
Reduced Order Model ◽  
Order Model ◽  
Data Set ◽  
Fluid Structure ◽  
Reduced Order ◽  
Structure Interaction ◽  
Bouncing Ball

AbstractThe aim of this work is to provide a reduced-order model to describe the dissipative behavior of nonlinear vertical sloshing involving Rayleigh–Taylor instability by means of a feed forward neural network. A 1-degree-of-freedom system is taken into account as representative of fluid–structure interaction problem. Sloshing has been replaced by an equivalent mechanical model, namely a boxed-in bouncing ball with parameters suitably tuned with performed experiments. A large data set, consisting of a long simulation of the bouncing ball model with pseudo-periodic motion of the boundary condition spanning different values of oscillation amplitude and frequency, is used to train the neural network. The obtained neural network model has been included in a Simulink®  environment for closed-loop fluid–structure interaction simulations showing promising performances for perspective integration in complex structural system.

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