Research ArticleOpen AccessGoogle Scholar indexed
Physics-Aware Deep Learning on Multiphase Flow Problems
Basis Independent Mclean, Mclean, USA
- 1 Basis Independent Mclean, Mclean, USA
Communications and Network·Volume 13 (2020)·Pages 1–11·Published 18 December 2020·DOI10.4236/cn.2021.131001
Copy link · social · email
Abstract
In this article, a physics aware deep learning model is introduced for multiphase flow problems. The deep learning model is shown to be capable of capturing complex physics phenomena such as saturation front, which is even challenging for numerical solvers due to the instability. We display the preciseness of the solution domain delivered by deep learning models and the low cost of deploying this model for complex physics problems, showing the versatile character of this method and bringing it to new areas. This will require more allocation points and more careful design of the deep learning model architectures and residual neural network can be a potential candidate.
KeywordsDeep LearningNeural NetworkMulti-PhaseOil IncompressibleFluid PhysicsPartial Differential Equation
- Moore, P.K. (1999) Finite Difference Methods and Spatial a Posteriori Error Estimates for Solving Parabolic Equations in Three Space Dimensions on Grids with Irregular Nodes. SIAM Journal on Numerical Analysis, 36, 1044-1064. https://doi.org/10.1137/S0036142997322072
- Burrage, K. and Tian, T.H. (2001) The Composite Euler Method for Stiff Stochastic Differential Equations. Journal of Computational and Applied Mathematics, 131, 407-426. https://doi.org/10.1016/S0377-0427(00)00259-4
- Khoo, Y., Lu, J.F. and Ying, L.X. (2017) Solving Parametric PDE Problems with Artificial Neural Networks.
- Raissi, M., Perdikaris, P. and Karniadakis, G.E. (2019) Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. Journal of Computational Physics, 378, 686-707. https://doi.org/10.1016/j.jcp.2018.10.045
- Brunton, S.L., Noack, B.R. and Koumoutsakos, P. (2020) Machine Learning for Fluid Mechanics. Annual Review of Fluid Mechanics, 52, 477-508. https://doi.org/10.1146/annurev-fluid-010719-060214
- Kissas, G., et al. (2020) Machine Learning in Cardiovascular Flows Modeling: Predicting Arterial Blood Pressure from Non-Invasive 4D Flow MRI Data Using Physics-Informed Neural Networks. Computer Methods in Applied Mechanics and Engineering, 358, Article ID: 112623. https://doi.org/10.1016/j.cma.2019.112623
- Maulik, R., et al. (2018) Data-Driven Deconvolution for Large Eddy Simulations of Kraichnan Turbulence. Physics of Fluids, 30, Article ID: 125109. https://doi.org/10.1063/1.5079582
- Gao, H., Sun, L.N. and Wang, J.-X. (2020) PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain.
- Jagtap, A.D., Kharazmi, E. and Karniadakis, G.E. (2020) Conservative Physics-Informed Neural Networks on Discrete Domains for Conservation Laws: Applications to Forward and Inverse Problems. Computer Methods in Applied Mechanics and Engineering, 365, Article ID: 113028. https://doi.org/10.1016/j.cma.2020.113028
- Meng, X.H., et al. (2020) PPINN: Parareal Physics-Informed Neural Network for Time-Dependent PDEs. Computer Methods in Applied Mechanics and Engineering, 370, Article ID: 113250. https://doi.org/10.1016/j.cma.2020.113250
- Guo, J., et al. (2020) GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. Journal of Machine Learning Research, 21, 1-7.