Pages that link to "Item:Q2125435"
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The following pages link to A derivative-free method for solving elliptic partial differential equations with deep neural networks (Q2125435):
Displaying 23 items.
- APFOS-Net: asymptotic preserving scheme for anisotropic elliptic equations with deep neural network (Q2133559) (← links)
- Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244) (← links)
- Solving elliptic equations with Brownian motion: bias reduction and temporal difference learning (Q2157396) (← links)
- Deep reinforcement learning of viscous incompressible flow (Q2162036) (← links)
- Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients (Q2246423) (← links)
- Adaptive deep neural networks methods for high-dimensional partial differential equations (Q2671349) (← links)
- A deep first-order system least squares method for solving elliptic PDEs (Q2679352) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- Actor-Critic Method for High Dimensional Static Hamilton--Jacobi--Bellman Partial Differential Equations based on Neural Networks (Q5021407) (← links)
- A Deep Learning Method for Elliptic Hemivariational Inequalities (Q5074898) (← links)
- A Local Deep Learning Method for Solving High Order Partial Differential Equations (Q5864768) (← links)
- An extreme learning machine-based method for computational PDEs in higher dimensions (Q6120177) (← links)
- DNN-HDG: a deep learning hybridized discontinuous Galerkin method for solving some elliptic problems (Q6158712) (← links)
- Subspace decomposition based DNN algorithm for elliptic type multi-scale PDEs (Q6162913) (← links)
- A Neural Network Approach for Homogenization of Multiscale Problems (Q6178099) (← links)
- Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← links)
- A Derivative-Free Method for Solving Elliptic Partial Differential Equations with Deep Neural Networks (Q6333024) (← links)
- Extremization to fine tune physics informed neural networks for solving boundary value problems (Q6591803) (← links)
- Connecting stochastic optimal control and reinforcement learning (Q6597633) (← links)
- Deep finite volume method for partial differential equations (Q6615033) (← links)
- Approximating the stationary Bellman equation by hierarchical tensor products (Q6616995) (← links)
- A stochastic approach for elliptic problems in perforated domains (Q6639325) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations (Q6645961) (← links)
