Pages that link to "Item:Q5106291"
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The following pages link to MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs (Q5106291):
Displaying 13 items.
- Data-driven deep learning of partial differential equations in modal space (Q2123370) (← links)
- ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains (Q2222287) (← links)
- An exploratory study on machine learning to couple numerical solutions of partial differential equations (Q2656809) (← links)
- Dosnet as a non-black-box PDE solver: when deep learning meets operator splitting (Q6095076) (← links)
- Capturing the diffusive behavior of the multiscale linear transport equations by asymptotic-preserving convolutional deeponets (Q6118592) (← links)
- Learning Specialized Activation Functions for Physics-Informed Neural Networks (Q6143615) (← links)
- DNN-HDG: a deep learning hybridized discontinuous Galerkin method for solving some elliptic problems (Q6158712) (← links)
- Render unto numerics: orthogonal polynomial neural operator for PDEs with nonperiodic boundary conditions (Q6575342) (← links)
- A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations (Q6584818) (← links)
- Asymptotic-preserving neural networks for multiscale kinetic equations (Q6585905) (← links)
- Bayesian inversion with neural operator (BINO) for modeling subdiffusion: forward and inverse problems (Q6593344) (← links)
- Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of a time fractional equation (Q6630929) (← links)
- MHDnet: physics-preserving learning for solving magnetohydrodynamics problems (Q6646462) (← links)
