Pages that link to "Item:Q1744192"

The following pages link to The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems (Q1744192):

Displaying 50 items.

• Solving traveltime tomography with deep learning (Q2699488) (← links)
• Neural network-based variational methods for solving quadratic porous medium equations in high dimensions (Q2699489) (← links)
• BI-GreenNet: learning Green's functions by boundary integral network (Q2699491) (← links)
• The Random Feature Model for Input-Output Maps between Banach Spaces (Q3382802) (← links)
• Neural networks for variational problems in engineering (Q3619739) (← links)
• Deep Splitting Method for Parabolic PDEs (Q4958922) (← links)
• The Gap between Theory and Practice in Function Approximation with Deep Neural Networks (Q4999396) (← links)
• Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation (Q4999517) (← links)
• Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences (Q5001377) (← links)
• Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control (Q5005011) (← links)
• A multi-level procedure for enhancing accuracy of machine learning algorithms (Q5014840) (← links)
• Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
• Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations (Q5037569) (← links)
• Deep Adaptive Basis Galerkin Method for High-Dimensional Evolution Equations With Oscillatory Solutions (Q5038412) (← links)
• PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations (Q5045670) (← links)
• Generalization Error Analysis of Neural Networks with Gradient Based Regularization (Q5045671) (← links)
• Deep Domain Decomposition Methods: Helmholtz Equation (Q5045688) (← links)
• MIONet: Learning Multiple-Input Operators via Tensor Product (Q5048574) (← links)
• A deep neural network-based numerical method for solving contact problems (Q5052594) (← links)
• High Order Deep Neural Network for Solving High Frequency Partial Differential Equations (Q5065177) (← links)
• VPVnet: A Velocity-Pressure-Vorticity Neural Network Method for the Stokes’ Equations under Reduced Regularity (Q5065192) (← links)
• An Augmented Lagrangian Deep Learning Method for Variational Problems with Essential Boundary Conditions (Q5065200) (← links)
• A Deep Learning Method for Elliptic Hemivariational Inequalities (Q5074898) (← links)
• Approximations with deep neural networks in Sobolev time-space (Q5075578) (← links)
• Convergence Rate Analysis for Deep Ritz Method (Q5077692) (← links)
• Deep Unfitted Nitsche Method for Elliptic Interface Problems (Q5077697) (← links)
• A Rate of Convergence of Physics Informed Neural Networks for the Linear Second Order Elliptic PDEs (Q5077701) (← links)
• A Consensus-Based Global Optimization Method with Adaptive Momentum Estimation (Q5077702) (← links)
• Imaging conductivity from current density magnitude using neural networks* (Q5081798) (← links)
• (Q5093541) (← links)
• Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification (Q5097855) (← links)
• (Q5104591) (← links)
• MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs (Q5106291) (← links)
• Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow (Q5106295) (← links)
• A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems (Q5132016) (← links)
• Deep ReLU networks and high-order finite element methods (Q5132226) (← links)
• Error bounds for approximations with deep ReLU neural networks in Ws,p norms (Q5132228) (← links)
• Numerical solution of inverse problems by weak adversarial networks (Q5132263) (← links)
• DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations (Q5161194) (← links)
• Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units (Q5162006) (← links)
• Finite Neuron Method and Convergence Analysis (Q5162357) (← links)
• Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks (Q5162358) (← links)
• A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales (Q5162363) (← links)
• Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains (Q5162368) (← links)
• On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370) (← links)
• Numerical Simulations for Full History Recursive Multilevel Picard Approximations for Systems of High-Dimensional Partial Differential Equations (Q5162373) (← links)
• Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains (Q5162374) (← links)
• Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning (Q5162375) (← links)
• Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection (Q5162626) (← links)