scholarly journals Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning

2021 ◽  
Author(s):  
Stefano Pagani ◽  
Andrea Manzoni
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Cardiac Electrophysiology ◽  
Reduced Order Modeling ◽  
Reduced Order

Machine learning–based reduced-order modeling of hydrodynamic forces using pressure mode decomposition

2021 ◽  
pp. 095441002199986
Author(s):  
Hassan F Ahmed ◽  
Hamayun Farooq ◽  
Imran Akhtar ◽  
Zafar Bangash
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Stokes Equations ◽  
Decomposition Analysis ◽  
Hydrodynamic Forces ◽  
Reduced Order Modeling ◽  
Navier Stokes ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Lift And Drag

In this article, we introduce a machine learning–based reduced-order modeling (ML-ROM) framework through the integration of proper orthogonal decomposition (POD) and deep neural networks (DNNs), in addition to long short-term memory (LSTM) networks. The DNN is utilized to upscale POD temporal coefficients and their respective spatial modes to account for the dynamics represented by the truncated modes. In the second part of the algorithm, temporal evolution of the POD coefficients is obtained by recursively predicting their future states using an LSTM network. The proposed model (ML-ROM) is tested for flow past a circular cylinder characterized by the Navier–Stokes equations. We perform pressure mode decomposition analysis on the flow data using both POD and ML-ROM to predict hydrodynamic forces and demonstrate the accuracy of the proposed strategy for modeling lift and drag coefficients.

scholarly journals Physics-informed machine learning for reduced-order modeling of nonlinear problems

2021 ◽  
Vol 446 ◽  
pp. 110666 ◽  
Author(s):  
Wenqian Chen ◽  
Qian Wang ◽  
Jan S. Hesthaven ◽  
Chuhua Zhang
Keyword(s):  
Machine Learning ◽  
Nonlinear Problems ◽  
Reduced Order Modeling ◽  
Reduced Order

Fast estimation of internal flowfields in scramjet intakes via reduced-order modeling and machine learning

2021 ◽  
Vol 33 (10) ◽  
pp. 106110
Author(s):  
Shuvayan Brahmachary ◽  
Ananthakrishnan Bhagyarajan ◽  
Hideaki Ogawa
Keyword(s):  
Machine Learning ◽  
Reduced Order Modeling ◽  
Reduced Order ◽  
Fast Estimation

Model fusion with physics-guided machine learning: Projection-based reduced-order modeling

2021 ◽  
Vol 33 (6) ◽  
pp. 067123
Author(s):  
Suraj Pawar ◽  
Omer San ◽  
Aditya Nair ◽  
Adil Rasheed ◽  
Trond Kvamsdal
Keyword(s):  
Machine Learning ◽  
Reduced Order Modeling ◽  
Model Fusion ◽  
Reduced Order
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