A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced‐order model

2021 ◽  
Author(s):  
Yang Zhou ◽  
Yuan He ◽  
Jianwei Wu ◽  
Chang Cui ◽  
Minglong Chen ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Deep Learning ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Cardiovascular Hemodynamics

scholarly journals Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhe Liu ◽  
Fangli Ning ◽  
Hui Ding ◽  
Qingbo Zhai ◽  
Juan Wei
Keyword(s):  
Deep Learning ◽  
Cavity Flow ◽  
Reduced Order Model ◽  
Flow Conditions ◽  
Order Model ◽  
Reduced Order ◽  
The Future ◽  
Flow Oscillations ◽  
Mach Numbers ◽  
Cavity Flow Oscillations

The reduced-order model can accurately and efficiently predict unsteady problems in many aerospace engineering applications. The traditional reduced-order model based on proper orthogonal decomposition (POD) and Galerkin projection has poor robustness and large error in predicting complex problems. In this paper, a reduced-order model combining POD and deep learning is proposed to predict cavity flow oscillations under different flow conditions. Firstly, POD modes and corresponding coefficients are obtained by POD. Then, two deep learning frameworks, including multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks, are used to predict the future POD coefficients, respectively. Finally, the cavity flow oscillations across multi-Mach numbers are predicted by the POD modes and the future coefficients. The results show that both of these frameworks can accurately predict cavity flow oscillations when the flow conditions change, and the time cost is reduced by order of magnitude. In addition, due to the performance of LSTM is better than that of MLP, its calculation speed is faster.

Deep Learning Based Reduced Order Model for Airfoil-Gust and Aeroelastic Interaction

AIAA Journal ◽  
2020 ◽  
Vol 58 (10) ◽  
pp. 4304-4321 ◽  
Author(s):  
R. Halder ◽  
M. Damodaran ◽  
B. C. Khoo
Keyword(s):  
Deep Learning ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order

Reduced-Order Modeling for Multiphase Flow Using a Physics-Based Deep Learning

2021 ◽  
Author(s):  
Thamer Alsulaimani ◽  
Mary Wheeler
Keyword(s):  
Deep Learning ◽  
Multiphase Flow ◽  
Loss Function ◽  
Dimensional Space ◽  
Mass Conservation ◽  
Reduced Order Model ◽  
High Dimensional ◽  
Order Model ◽  
High Dimensional Space ◽  
Reduced Order

Abstract Reservoir simulation is the most widely used tool for oil and gas production forecasting and reservoir management. Solving a large-scale system of nonlinear differential equations every timestep can be computationally expensive. In this work, we present a two-phase physics-constrained deep-learning reduced-order model as a surrogate model for subsurface flow production forecast. The implemented deep learning model is a physics-guided encoder-decoder, constructed based on the Embed-to-Control (E2C) framework. In our implementation, the E2C works in a way analogous to Proper Orthogonal Decomposition combined with Discrete Empirical Interpolation Method (POD-DEIM) or Trajectory Piece-Wise Linearization approach (POD-TPWL). The E2C-Reduced-order model (ROM) involves projecting the system from a high-dimensional space into a low-dimensional subspace using the encoder-decoder, approximating the progression of the system from one timestep to the next using a linear transition model, and finally projecting the system back to high-dimensional space using the encoder-decoder. To guarantee mass conservation, we adopt the Finite Elements Mixed Formulation in the neural network's loss function combined with the original data-based loss function. Training simulations are generated using a full-physics reservoir simulator (IPARS). High-fidelity pressure, velocity, and saturation solution snapshot at constant time intervals are taken as training input to the neural network. After training, the model is tested over large variations of well control settings. Accurate pressure and saturation solutions are predicted along with the injection and production well quantities using the proposed approach. Errors in the predicted quantities of interest are reduced with the increase in the number of training simulations used. Although it required a large number of training simulations for the offline (training) step, the model achieved a significant speedup in the online stage compared to the full physics model. Considering the overall computational cost, this ROM model is proper for cases when a large number of simulations are required like in the case of production optimization and uncertainty assessments. The proposed approach shows the capability of the deep-learning reduced-order model to accurately predict multiphase flow behavior such as well quantities, and global pressure and saturation fields. The model honors mass conservation and the underlying physics laws, which many existing approaches don't take into direct consideration.

scholarly journals A non-intrusive parametric reduced order model for urban wind flow using deep learning and Grassmann manifold.

2021 ◽  
Vol 2018 (1) ◽  
pp. 012038
Author(s):  
Mandar V Tabib ◽  
Suraj Pawar ◽  
Shady E. Ahmed ◽  
Adil Rasheed ◽  
Omer San
Keyword(s):  
Deep Learning ◽  
Grassmann Manifold ◽  
Reduced Order Model ◽  
Wind Flow ◽  
Order Model ◽  
Reduced Order
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