Journal Article10.1016/J.CMA.2019.112766
Data-driven reduced order model with temporal convolutional neural network
Pin Wu,Pin Wu,Junwu Sun,Xuting Chang,Wenjie Zhang,Rossella Arcucci,Yike Guo,Yike Guo,Christopher C. Pain +8 more
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TL;DR: A novel model reduction method based on proper orthogonal decomposition and temporal convolutional neural network that depends only on the solution of flow field to construct reduced order model is presented.
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About: This article is published in Computer Methods in Applied Mechanics and Engineering. The article was published on 01 Mar 2020. The article focuses on the topics: Deep learning & Convolutional neural network.
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Citations
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
TL;DR: The results suggest that the accuracy of NSFnets, for both laminar and turbulent flows, can be improved with proper tuning of weights (manual or dynamic) in the loss function.
738
Deep Learning for Time Series Forecasting: A Survey.
José F. Torres,Dalil Hadjout,Abderrazak Sebaa,Francisco Martínez-Álvarez,Alicia Troncoso +4 more
- 05 Feb 2021
TL;DR: The most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations.
586
On closures for reduced order models—A spectrum of first-principle to machine-learned avenues
Shady E. Ahmed,Suraj Pawar,Omer San,Adil Rasheed,Traian Iliescu,Bernd R. Noack,Bernd R. Noack +6 more
TL;DR: In this article, the effect of the discarded reduced order modes in under-resolved simulations is modeled using data-driven proper orthogonal decomposition (POD) modeling.
149
Temporal convolutional network with soft thresholding and attention mechanism for machinery prognostics
TL;DR: A new temporal convolutional neural network with soft threshold and attention mechanism is proposed for machinery prognostics with good robustness and generalization ability, and is compared with several state-of-the-art prognostic approaches.
95
On closures for reduced order models $-$ A spectrum of first-principle to machine-learned avenues
Shady E. Ahmed,Suraj Pawar,Omer San,Adil Rasheed,Traian Iliescu,Bernd R. Noack,Bernd R. Noack +6 more
TL;DR: In this paper, the authors focus on the effect of the discarded reduced order modes in under-resolved simulations and show how data-driven modeling, artificial intelligence, and machine learning have changed the standard reduced order modeling methodology over the last two decades.
89
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