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.

• Deep Ritz method for elliptical multiple eigenvalue problems (Q6182319) (← links)
• Local randomized neural networks with discontinuous Galerkin methods for diffusive-viscous wave equation (Q6184721) (← links)
• Multi-level neural networks for accurate solutions of boundary-value problems (Q6185225) (← links)
• High Order Deep Domain Decomposition Method for Solving High Frequency Interface Problems (Q6188658) (← links)
• An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations (Q6191768) (← links)
• DNN-MG: a hybrid neural network/finite element method with applications to 3D simulations of the Navier-Stokes equations (Q6194150) (← links)
• Physics-informed graph neural network emulation of soft-tissue mechanics (Q6194151) (← links)
• The random feature method for solving interface problems (Q6194187) (← links)
• AONN: An Adjoint-Oriented Neural Network Method for All-At-Once Solutions of Parametric Optimal Control Problems (Q6194971) (← links)
• Neural Control of Parametric Solutions for High-Dimensional Evolution PDEs (Q6194975) (← links)
• Optimal Dirichlet boundary control by Fourier neural operators applied to nonlinear optics (Q6196628) (← links)
• A Reduced Order Schwarz Method for Nonlinear Multiscale Elliptic Equations Based on Two-Layer Neural Networks (Q6197994) (← links)
• Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent? (Q6198233) (← links)
• The mathematics of artificial intelligence (Q6200206) (← links)
• Solving inverse problems with deep learning (Q6200208) (← links)
• Data-driven Whitney forms for structure-preserving control volume analysis (Q6202128) (← links)
• Operator approximation of the wave equation based on deep learning of Green's function (Q6202600) (← links)
• Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions (Q6202605) (← links)
• Error assessment of an adaptive finite elements -- neural networks method for an elliptic parametric PDE (Q6202970) (← links)
• Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions (Q6204733) (← links)
• Convergence rate of DeepONets for learning operators arising from advection-diffusion equations (Q6361196) (← links)
• Basis operator network: a neural network-based model for learning nonlinear operators via neural basis (Q6488825) (← links)
• CCGnet: a deep learning approach to predict Nash equilibrium of chance-constrained games (Q6492614) (← links)
• Adaptive quadratures for nonlinear approximation of low-dimensional PDEs using smooth neural networks (Q6494202) (← links)
• Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations (Q6497269) (← links)
• Physical informed memory networks for solving PDEs: implementation and applications (Q6497869) (← links)
• Latent assimilation with implicit neural representations for unknown dynamics (Q6498490) (← links)
• Deep neural network solutions for oscillatory Fredholm integral equations (Q6539318) (← links)
• Constraint free physics-informed machine learning for micromagnetic energy minimization (Q6543808) (← links)
• Adaptive deep neural networks for solving corner singular problems (Q6545698) (← links)
• Investigating deep energy method applications in thermoelasticity (Q6545726) (← links)
• A nonlocal energy-informed neural network for peridynamic correspondence material models (Q6545792) (← links)
• Learning based numerical methods for acoustic frequency-domain simulation with high frequency (Q6545926) (← links)
• Dynamically configured physics-informed neural network in topology optimization applications (Q6550166) (← links)
• Meshless physics-informed deep learning method for three-dimensional solid mechanics (Q6554056) (← links)
• Fourier neural operator based fluid-structure interaction for predicting the vesicle dynamics (Q6554917) (← links)
• Neural-integrated meshfree (NIM) method: a differentiable programming-based hybrid solver for computational mechanics (Q6557785) (← links)
• A neural network finite element approach for high speed cardiac mechanics simulations (Q6557830) (← links)
• Local neural operator for solving transient partial differential equations on varied domains (Q6557832) (← links)
• N-adaptive Ritz method: a neural network enriched partition of unity for boundary value problems (Q6566038) (← links)
• Gabor-filtered Fourier neural operator for solving partial differential equations (Q6566939) (← links)
• TDOR-MPINNs: multi-output physics-informed neural networks based on time differential order reduction for solving coupled Klein-Gordon-Zakharov systems (Q6568897) (← links)
• Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons (Q6569679) (← links)
• Neuro-PINN: a hybrid framework for efficient nonlinear projection equation solutions (Q6569923) (← links)
• AONN-2: an adjoint-oriented neural network method for PDE-constrained shape optimization (Q6572176) (← links)
• Koopman neural operator as a mesh-free solver of non-linear partial differential equations (Q6572200) (← links)
• Enhancing physics informed neural networks for solving Navier-Stokes equations (Q6574171) (← links)
• Meta-auto-decoder: a meta-learning-based reduced order model for solving parametric partial differential equations (Q6575297) (← links)
• Optimization of random feature method in the high-precision regime (Q6575315) (← links)
• Render unto numerics: orthogonal polynomial neural operator for PDEs with nonperiodic boundary conditions (Q6575342) (← links)