Pages that link to "Item:Q2133607"
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The following pages link to MIM: a deep mixed residual method for solving high-order partial differential equations (Q2133607):
Displaying 26 items.
- A deep domain decomposition method based on Fourier features (Q2112697) (← links)
- A deep first-order system least squares method for solving elliptic PDEs (Q2679352) (← links)
- BI-GreenNet: learning Green's functions by boundary integral network (Q2699491) (← links)
- Solving multiscale elliptic problems by sparse radial basis function neural networks (Q6054222) (← links)
- Randomized neural network with Petrov-Galerkin methods for solving linear and nonlinear partial differential equations (Q6058946) (← links)
- Numerical solution of the modified and non-Newtonian Burgers equations by stochastic coded trees (Q6072375) (← links)
- Capturing the diffusive behavior of the multiscale linear transport equations by asymptotic-preserving convolutional deeponets (Q6118592) (← links)
- DNN-HDG: a deep learning hybridized discontinuous Galerkin method for solving some elliptic problems (Q6158712) (← links)
- Asymptotic-preserving neural networks for multiscale time-dependent linear transport equations (Q6158979) (← links)
- Adaptive Learning Rate Residual Network Based on Physics-Informed for Solving Partial Differential Equations (Q6173072) (← links)
- A priori error estimate of deep mixed residual method for elliptic PDEs (Q6182315) (← links)
- An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations (Q6191768) (← links)
- A deep branching solver for fully nonlinear partial differential equations (Q6196609) (← links)
- Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions (Q6202605) (← links)
- MIM: A deep mixed residual method for solving high-order partial differential equations (Q6342285) (← links)
- A conservative and positivity-preserving method for solving anisotropic diffusion equations with deep learning (Q6537080) (← links)
- Gradient auxiliary physics-informed neural network for nonlinear biharmonic equation (Q6540123) (← links)
- Adaptive deep neural networks for solving corner singular problems (Q6545698) (← links)
- Render unto numerics: orthogonal polynomial neural operator for PDEs with nonperiodic boundary conditions (Q6575342) (← links)
- Temporal difference learning for high-dimensional PIDEs with jumps (Q6575343) (← links)
- Asymptotic-preserving neural networks for multiscale kinetic equations (Q6585905) (← links)
- Deep finite volume method for partial differential equations (Q6615033) (← links)
- Higher-order multi-scale physics-informed neural network (HOMS-PINN) method and its convergence analysis for solving elastic problems of authentic composite materials (Q6633295) (← links)
- A new method to compute the blood flow equations using the physics-informed neural operator (Q6639295) (← links)
- Error analysis of the mixed residual method for elliptic equations (Q6662404) (← links)
- Deep mixed residual method for solving PDE-constrained optimization problems (Q6663447) (← links)
