Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Mustafa Mustafa
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
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Physics-guided deep learning for generating turbulent inflow conditions
Mustafa Z . Yousif
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A gradient-based deep neural network model for simulating multiphase flow in porous media
Hussein Hoteit
Journal of Computational Physics
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ML-LBM: Machine Learning Aided Flow Simulation in Porous Media
Traiwit Chung
ArXiv, 2020
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Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks
Mpoki Sam Mwasamwasa
Cmes-computer Modeling in Engineering & Sciences, 2023
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Deep learning for turbulent channel flow
David Sondak
2018
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Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media
Wen Pan
Transport in Porous Media, 2021
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Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks
Maryam Aliakbari
International Journal of Heat and Fluid Flow, 2022
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Fourier Neural Operator for Fluid Flow in Small-Shape 2D Simulated Porous Media Dataset
abouzar choubineh
Algorithms
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Residual-based adaptivity for two-phase flow simulation in porous media using Physics-informed Neural Networks
John Hanna
Computer Methods in Applied Mechanics and Engineering
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Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach
NAM LUU DINH
Nuclear Science and Engineering, 2020
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Encoder–Decoder Convolutional Neural Networks for Flow Modeling in Unsaturated Porous Media: Forward and Inverse Approaches
Nima Kamali
Water
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Towards an hybrid computational strategy based on Deep Learning for incompressible flows
Antonio Alguacil
AIAA AVIATION 2020 FORUM, 2020
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Predicting Effective Diffusivity of Porous Media from Images by Deep Learning
Rui Qiao
Scientific Reports
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Effect of network architecture on physics-informed deep learning of the Reynolds-averaged turbulent flow field around cylinders without training data
Franz-Josef Peitzmann
Frontiers in physics, 2024
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Robust deep learning for emulating turbulent viscosities
George El haber
2021
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Physics-driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks
Oskar Haidn
2021
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Deep learning model for two-fluid flows
George El haber
Physics of Fluids
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Turbulence Modeling for Physics-Informed Neural Networks: Comparison of Different RANS Models for the Backward-Facing Step Flow
Franz-Josef Peitzmann
Fluids
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Deep learning for diffusion in porous media
Dawid Strzelczyk
arXiv (Cornell University), 2023
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Predicting porosity, permeability, and tortuosity of porous media from images by deep learning
Maciej Matyka
Scientific Reports, 2020
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DEEP LEARNING-BASED TURBULENCE CLOSURE WITH IMPROVED OPTIMAL EDDY VISCOSITY PREDICTION
Eugene Yee
Proceedings of the 29th Annual Conference of the Computational Fluid Dynamics Society of Canada, 2021
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Leveraging machine learning in porous media
Hossein Mashhadimoslem
Journal of Materials Chemistry A, 2024
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Reynolds-Averaged Turbulence Modeling Using Type I and Type II Machine Learning Frameworks with Deep Learning
Nam Dinh
arXiv: Fluid Dynamics, 2018
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Deep Multilayer Convolution Frameworks for Data-Driven Learning of Fluid flow Dynamics
Balaji Jayaraman
2018 Fluid Dynamics Conference, 2018
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Deep structured neural networks for turbulence closure modeling
Eugene Yee
Physics of Fluids, 2022
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Physics-informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media
Pingki Datta
Cornell University - arXiv, 2022
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Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning
Nam Đinh
International Journal of Multiphase Flow, 2020
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