scholarly journals A deep learning enabler for nonintrusive reduced order modeling of fluid flows

2019 ◽  
Vol 31 (8) ◽  
pp. 085101 ◽  
Author(s):  
S. Pawar ◽  
S. M. Rahman ◽  
H. Vaddireddy ◽  
O. San ◽  
A. Rasheed ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluid Flows ◽  
Reduced Order Modeling ◽  
Reduced Order

scholarly journals Data-Driven Filtered Reduced Order Modeling of Fluid Flows

2018 ◽  
Vol 40 (3) ◽  
pp. B834-B857 ◽  
Author(s):  
X. Xie ◽  
M. Mohebujjaman ◽  
L. G. Rebholz ◽  
T. Iliescu
Keyword(s):  
Fluid Flows ◽  
Reduced Order Modeling ◽  
Data Driven ◽  
Reduced Order

Reduced-Order Modeling of Fluid Flows in Closed-Loop Systems

2019 ◽  
Author(s):  
C. Fiorina ◽  
P. German ◽  
J. Ragusa ◽  
M. Tano
Keyword(s):  
Closed Loop ◽  
Fluid Flows ◽  
Reduced Order Modeling ◽  
Reduced Order ◽  
Closed Loop Systems

Reduced-Order Flood Modeling Using Uncertainty-Aware Deep Neural Networks

2020 ◽  
Author(s):  
Pierre Jacquier ◽  
Azzedine Abdedou ◽  
Azzeddine Soulaïmani
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Uncertainty Quantification ◽  
Surrogate Model ◽  
Deep Neural Networks ◽  
Reduced Order Modeling ◽  
Flood Modeling ◽  
Reduced Order ◽  
Highly Nonlinear ◽  
The Impact

<p><strong>Key Words</strong>: Uncertainty Quantification, Deep Learning, Space-Time POD, Flood Modeling</p><p><br>While impressive results have been achieved in the well-known fields where Deep Learning allowed for breakthroughs such as computer vision, language modeling, or content generation [1], its impact on different, older fields is still vastly unexplored. In computational fluid dynamics and especially in Flood Modeling, many phenomena are very high-dimensional, and predictions require the use of finite element or volume methods, which can be, while very robust and tested, computational-heavy and may not prove useful in the context of real-time predictions. This led to various attempts at developing Reduced-Order Modeling techniques, both intrusive and non-intrusive. One late relevant addition was a combination of Proper Orthogonal Decomposition with Deep Neural Networks (POD-NN) [2]. Yet, to our knowledge, in this example and more generally in the field, little work has been conducted on quantifying uncertainties through the surrogate model.<br>In this work, we aim at comparing different novel methods addressing uncertainty quantification in reduced-order models, pushing forward the POD-NN concept with ensembles, latent-variable models, as well as encoder-decoder models. These are tested on benchmark problems, and then applied to a real-life application: flooding predictions in the Mille-Iles river in Laval, QC, Canada.<br>For the flood modeling application, our setup involves a set of input parameters resulting from onsite measures. High-fidelity solutions are then generated using our own finite-volume code CuteFlow, which is solving the highly nonlinear Shallow Water Equations. The goal is then to build a non-intrusive surrogate model, that’s able to <em>know what it know</em>s, and more importantly, <em>know when it doesn’t</em>, which is still an open research area as far as neural networks are concerned [3].</p><p><br><strong>REFERENCES</strong><br>[1] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning”, in Thirty-First AAAI Conference on Artificial Intelligence, 2017.<br>[2] Q. Wang, J. S. Hesthaven, and D. Ray, “Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem”, Journal of Computational Physics, vol. 384, pp. 289–307, May 2019.<br>[3] B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles”, in Advances in Neural Information Processing Systems, 2017, pp. 6402–6413.</p>

scholarly journals Commutation error in reduced order modeling of fluid flows

2019 ◽  
Vol 45 (5-6) ◽  
pp. 2587-2621 ◽  
Author(s):  
Birgul Koc ◽  
Muhammad Mohebujjaman ◽  
Changhong Mou ◽  
Traian Iliescu
Keyword(s):  
Fluid Flows ◽  
Reduced Order Modeling ◽  
Reduced Order

scholarly journals Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data

2018 ◽  
Vol 31 (1) ◽  
pp. 58-65 ◽  
Author(s):  
Zhen Zhang ◽  
Xu-dong Song ◽  
Shu-ran Ye ◽  
Yi-wei Wang ◽  
Chen-guang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reynolds Stress ◽  
Channel Flow ◽  
Reduced Order Modeling ◽  
Learning Method ◽  
Reduced Order ◽  
Stress Models

scholarly journals Data-driven recovery of hidden physics in reduced order modeling of fluid flows

2020 ◽  
Vol 32 (3) ◽  
pp. 036602 ◽  
Author(s):  
Suraj Pawar ◽  
Shady E. Ahmed ◽  
Omer San ◽  
Adil Rasheed
Keyword(s):  
Fluid Flows ◽  
Reduced Order Modeling ◽  
Data Driven ◽  
Reduced Order
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