Pages that link to "Item:Q5162368"

The following pages link to Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains (Q5162368):

Displaying 50 items.

• FBSDE based neural network algorithms for high-dimensional quasilinear parabolic PDEs (Q2083635) (← links)
• INN: interfaced neural networks as an accessible meshless approach for solving interface PDE problems (Q2083675) (← links)
• A deep domain decomposition method based on Fourier features (Q2112697) (← links)
• Using neural networks to accelerate the solution of the Boltzmann equation (Q2132591) (← links)
• SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs (Q2133032) (← links)
• Meta-mgnet: meta multigrid networks for solving parameterized partial differential equations (Q2133752) (← links)
• DeLISA: deep learning based iteration scheme approximation for solving PDEs (Q2134800) (← links)
• Multi-scale fusion network: a new deep learning structure for elliptic interface problems (Q2691986) (← links)
• Generalization Error Analysis of Neural Networks with Gradient Based Regularization (Q5045671) (← links)
• On the Exact Computation of Linear Frequency Principle Dynamics and Its Generalization (Q5051354) (← links)
• High Order Deep Neural Network for Solving High Frequency Partial Differential Equations (Q5065177) (← links)
• An Augmented Lagrangian Deep Learning Method for Variational Problems with Essential Boundary Conditions (Q5065200) (← links)
• MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs (Q5106291) (← links)
• A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales (Q5162363) (← links)
• Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains (Q5162374) (← links)
• Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions (Q5163229) (← links)
• The model reduction of the Vlasov–Poisson–Fokker–Planck system to the Poisson–Nernst–Planck system <i>via</i> the Deep Neural Network Approach (Q5163496) (← links)
• A Local Deep Learning Method for Solving High Order Partial Differential Equations (Q5864768) (← links)
• Stationary Density Estimation of Itô Diffusions Using Deep Learning (Q5886225) (← 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)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• Linearized Learning with Multiscale Deep Neural Networks for Stationary Navier-Stokes Equations with Oscillatory Solutions (Q6069455) (← links)
• Computing non-equilibrium trajectories by a deep learning approach (Q6095083) (← links)
• A dimension-augmented physics-informed neural network (DaPINN) with high level accuracy and efficiency (Q6095102) (← links)
• Learning high frequency data via the coupled frequency predictor-corrector triangular DNN (Q6104304) (← links)
• Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning (Q6108164) (← links)
• A Correction and Comments on “Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains CiCP, 28(5):1970–2001,2020” (Q6111318) (← links)
• A shallow physics-informed neural network for solving partial differential equations on static and evolving surfaces (Q6118543) (← links)
• Physics-informed neural network frameworks for crack simulation based on minimized peridynamic potential energy (Q6153887) (← links)
• Solving the Boltzmann Equation with a Neural Sparse Representation (Q6154197) (← links)
• Subspace decomposition based DNN algorithm for elliptic type multi-scale PDEs (Q6162913) (← links)
• Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (Q6171723) (← links)
• Adaptive Learning Rate Residual Network Based on Physics-Informed for Solving Partial Differential Equations (Q6173072) (← links)
• On the spectral bias of coupled frequency predictor-corrector triangular DNN: the convergence analysis (Q6179933) (← links)
• Multi-level neural networks for accurate solutions of boundary-value problems (Q6185225) (← links)
• Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions (Q6202605) (← links)
• Zero coordinate shift: whetted automatic differentiation for physics-informed operator learning (Q6497254) (← links)
• Learning based numerical methods for acoustic frequency-domain simulation with high frequency (Q6545926) (← links)
• A causality-DeepONet for causal responses of linear dynamical systems (Q6584819) (← links)
• Solving parametric elliptic interface problems via interfaced operator network (Q6589882) (← links)
• Iterative algorithms for partitioned neural network approximation to partial differential equations (Q6590244) (← links)
• Bayesian inversion with neural operator (BINO) for modeling subdiffusion: forward and inverse problems (Q6593344) (← links)
• A block-coordinate approach of multi-level optimization with an application to physics-informed neural networks (Q6624433) (← links)
• Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of a time fractional equation (Q6630929) (← links)
• Approximation and generalization of DeepONets for learning operators arising from a class of singularly perturbed problems (Q6630935) (← 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)
• MHDnet: physics-preserving learning for solving magnetohydrodynamics problems (Q6646462) (← links)
• Loss jump during loss switch in solving PDEs with neural networks (Q6646469) (← links)
• An immersed interface neural network for elliptic interface problems (Q6664871) (← links)