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.

• Temporal difference learning for high-dimensional PIDEs with jumps (Q6575343) (← links)
• Cell-average based neural network method for Hunter-Saxton equations (Q6578103) (← links)
• Deep learning based on randomized quasi-Monte Carlo method for solving linear Kolmogorov partial differential equation (Q6582041) (← links)
• Darboux transformation-based LPNN generating novel localized wave solutions (Q6584190) (← links)
• A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations (Q6584818) (← links)
• A causality-DeepONet for causal responses of linear dynamical systems (Q6584819) (← links)
• Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
• Solving Poisson problems in polygonal domains with singularity enriched physics informed neural networks (Q6585303) (← links)
• Domain decomposition learning methods for solving elliptic problems (Q6585310) (← links)
• Energetic variational neural network discretizations of gradient flows (Q6585315) (← links)
• Adaptive sampling points based multi-scale residual network for solving partial differential equations (Q6585372) (← links)
• Asymptotic-preserving neural networks for multiscale kinetic equations (Q6585905) (← links)
• Generalization error in the deep Ritz method with smooth activation functions (Q6585908) (← links)
• A pre-training deep learning method for simulating the large bending deformation of bilayer plates (Q6586297) (← links)
• Phase-field modeling of fracture with physics-informed deep learning (Q6588261) (← links)
• Variational temporal convolutional networks for I-FENN thermoelasticity (Q6588274) (← links)
• Partitioned neural network approximation for partial differential equations enhanced with Lagrange multipliers and localized loss functions (Q6588333) (← links)
• High-dimensional stochastic control models for newsvendor problems and deep learning resolution (Q6589107) (← links)
• Deep JKO: time-implicit particle methods for general nonlinear gradient flows (Q6589858) (← links)
• Inf-sup neural networks for high-dimensional elliptic PDE problems (Q6589859) (← links)
• Learning-based multi-continuum model for multiscale flow problems (Q6589888) (← links)
• Least-squares neural network (LSNN) method for linear advection-reaction equation: discontinuity interface (Q6590129) (← links)
• Iterative algorithms for partitioned neural network approximation to partial differential equations (Q6590244) (← links)
• Proof of the theory-to-practice gap in deep learning via sampling complexity bounds for neural network approximation spaces (Q6592113) (← links)
• Decoupling numerical method based on deep neural network for nonlinear degenerate interface problems (Q6592742) (← links)
• Bayesian inversion with neural operator (BINO) for modeling subdiffusion: forward and inverse problems (Q6593344) (← links)
• Failure-informed adaptive sampling for PINNs. II: Combining with re-sampling and subset simulation (Q6593776) (← links)
• Physics-informed deep learning of rate-and-state fault friction (Q6595877) (← links)
• Transfer learning enhanced nonlocal energy-informed neural network for quasi-static fracture in rock-like materials (Q6595896) (← links)
• Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning (Q6598418) (← links)
• Pseudo-differential integral autoencoder network for inverse PDE operators (Q6601306) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← links)
• Solving partial differential equations by LS-SVM (Q6606433) (← links)
• Convergence analysis for over-parameterized deep learning (Q6608346) (← links)
• Multistep asymptotic pre-training strategy based on PINNs for solving steep boundary singular perturbation problems (Q6609750) (← links)
• Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity (Q6609781) (← links)
• Fractional weak adversarial networks for the stationary fractional advection dispersion equations (Q6612980) (← links)
• Solving high-dimensional parametric engineering problems for inviscid flow around airfoils based on physics-informed neural networks (Q6615001) (← links)
• Deep finite volume method for partial differential equations (Q6615033) (← links)
• Recent developments in machine learning methods for stochastic control and games (Q6615618) (← links)
• Randomized neural network methods for solving obstacle problems (Q6621121) (← links)
• Enhancing training of physics-informed neural networks using domain decomposition-based preconditioning strategies (Q6623675) (← links)
• An accurate and efficient continuity-preserved method based on randomized neural networks for elliptic interface problems (Q6623701) (← links)
• A block-coordinate approach of multi-level optimization with an application to physics-informed neural networks (Q6624433) (← links)
• Unsupervised random quantum networks for PDEs (Q6629259) (← links)
• Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of a time fractional equation (Q6630929) (← 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)
• PDE generalization of in-context operator networks: a study on 1D scalar nonlinear conservation laws (Q6639294) (← links)
• A new method to compute the blood flow equations using the physics-informed neural operator (Q6639295) (← links)
• Solving high-dimensional partial differential equations using tensor neural network and a posteriori error estimators (Q6639507) (← links)