Pages that link to "Item:Q2237777"
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The following pages link to Theory-guided auto-encoder for surrogate construction and inverse modeling (Q2237777):
Displaying 9 items.
- Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models (Q2112502) (← links)
- Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology (Q2683244) (← links)
- HRW: Hybrid Residual and Weak Form Loss for Solving Elliptic Interface Problems with Neural Network (Q6151336) (← links)
- Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network (Q6181707) (← links)
- Data-driven modelling with coarse-grid network models (Q6557138) (← links)
- Physics-informed multi-grid neural operator: theory and an application to porous flow simulation (Q6648363) (← links)
- Prediction of spatiotemporal dynamics using deep learning: coupled neural networks of long short-terms memory, auto-encoder and physics-informed neural networks (Q6650113) (← links)
