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1908.05823
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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
16 August 2019
Meng Tang
Yimin Liu
L. Durlofsky
AI4CE
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Papers citing
"A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems"
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Title
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Deep learning surrogate models of JULES-INFERNO for wildfire prediction on a global scale
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Accelerated training of deep learning surrogate models for surface displacement and flow, with application to MCMC-based history matching of CO2 storage operations
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27 Jun 2024
Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
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Yuntian Chen
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07 Jun 2024
Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction
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Efficient deep data assimilation with sparse observations and time-varying sensors
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Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media
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F. J. Hickernell
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20 Oct 2023
Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
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High Throughput Training of Deep Surrogates from Large Ensemble Runs
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Performance Comparison of Design Optimization and Deep Learning-based Inverse Design
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Graph Convolutional Networks for Simulating Multi-phase Flow and Transport in Porous Media
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B. Guo
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10 Jul 2023
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X. Ju
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Physics-informed Convolutional Recurrent Surrogate Model for Reservoir Simulation with Well Controls
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Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process
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Chao Hu
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Learning to Accelerate Partial Differential Equations via Latent Global Evolution
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Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
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Qinchen Wang
Yinan Zhang
Rex Ying
Kaidi Cao
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15 Jun 2022
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net
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Dongxiao Zhang
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Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models
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Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization
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Deep learning based closed-loop optimization of geothermal reservoir production
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Convolutional-Recurrent Neural Network Proxy for Robust Optimization and Closed-Loop Reservoir Management
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Machine Learning in Heterogeneous Porous Materials
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Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
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U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow
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A Robust Deep Learning Workflow to Predict Multiphase Flow Behavior during Geological CO2 Sequestration Injection and Post-Injection Periods
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