Pages that link to "Item:Q1744192"
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The following pages link to The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems (Q1744192):
Displaying 50 items.
- Capturing the diffusive behavior of the multiscale linear transport equations by asymptotic-preserving convolutional deeponets (Q6118592) (← links)
- Pseudo-Hamiltonian neural networks for learning partial differential equations (Q6119277) (← links)
- An extreme learning machine-based method for computational PDEs in higher dimensions (Q6120177) (← links)
- Limitations of neural network training due to numerical instability of backpropagation (Q6122651) (← links)
- Transferable neural networks for partial differential equations (Q6123346) (← links)
- Weak adversarial networks for solving forward and inverse problems involving 2D incompressible Navier-Stokes equations (Q6125404) (← links)
- A conservative hybrid deep learning method for Maxwell-Ampère-Nernst-Planck equations (Q6126572) (← links)
- Deep learning-based schemes for singularly perturbed convection-diffusion problems (Q6127045) (← links)
- A new method for solving nonlinear partial differential equations based on liquid time-constant networks (Q6130983) (← links)
- Random vibration of hysteretic systems under Poisson white noise excitations (Q6132251) (← links)
- Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems (Q6132292) (← links)
- Deep convolutional Ritz method: parametric PDE surrogates without labeled data (Q6132294) (← links)
- A dive into spectral inference networks: improved algorithms for self-supervised learning of continuous spectral representations (Q6132297) (← links)
- A posteriori error control of approximate solutions to boundary value problems found by neural networks (Q6132492) (← links)
- A Rate of Convergence of Weak Adversarial Neural Networks for the Second Order Parabolic PDEs (Q6143000) (← links)
- A Novel Deep Neural Network Algorithm for the Helmholtz Scattering Problem In the Unbounded Domain (Q6143278) (← links)
- Learning Specialized Activation Functions for Physics-Informed Neural Networks (Q6143615) (← links)
- Neural Networks with Local Converging Inputs (NNLCI) for Solving Conservation Laws, Part II: 2D Problems (Q6143616) (← links)
- A Variational Neural Network Approach for Glacier Modelling with Nonlinear Rheology (Q6143617) (← links)
- Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons (Q6143642) (← links)
- A Data-Assisted Two-Stage Method for the Inverse Random Source Problem (Q6144051) (← links)
- \(r\)-adaptive deep learning method for solving partial differential equations (Q6144172) (← links)
- Error analysis of deep Ritz methods for elliptic equations (Q6145797) (← links)
- Convergence Analysis of a Quasi-Monte CarloBased Deep Learning Algorithm for Solving Partial Differential Equations (Q6151262) (← links)
- MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling (Q6151271) (← links)
- HRW: Hybrid Residual and Weak Form Loss for Solving Elliptic Interface Problems with Neural Network (Q6151336) (← links)
- Improved Analysis of PINNs: Alleviate the CoD for Compositional Solutions (Q6151354) (← links)
- Stochastic dynamics and data science (Q6151506) (← links)
- Solving the Boltzmann Equation with a Neural Sparse Representation (Q6154197) (← links)
- Physics-informed variational inference for uncertainty quantification of stochastic differential equations (Q6158129) (← links)
- A deep neural network-based method for solving obstacle problems (Q6158276) (← links)
- DNN-HDG: a deep learning hybridized discontinuous Galerkin method for solving some elliptic problems (Q6158712) (← links)
- Asymptotic-preserving neural networks for multiscale time-dependent linear transport equations (Q6158979) (← links)
- Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine (Q6159015) (← links)
- ReLU neural network Galerkin BEM (Q6159305) (← links)
- HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis (Q6159332) (← links)
- Analytical derivatives of neural networks (Q6162825) (← links)
- Subspace decomposition based DNN algorithm for elliptic type multi-scale PDEs (Q6162913) (← links)
- Neural Network Method for Integral Fractional Laplace Equations (Q6165528) (← links)
- Machine learning architectures for price formation models (Q6166250) (← links)
- Automatic boundary fitting framework of boundary dependent physics-informed neural network solving partial differential equation with complex boundary conditions (Q6171169) (← links)
- A symmetry group based supervised learning method for solving partial differential equations (Q6171229) (← links)
- Exact Dirichlet boundary physics-informed neural network EPINN for solid mechanics (Q6171233) (← links)
- Adaptive Learning Rate Residual Network Based on Physics-Informed for Solving Partial Differential Equations (Q6173072) (← links)
- A Scalable Deep Learning Approach for Solving High-Dimensional Dynamic Optimal Transport (Q6175117) (← links)
- Failure-Informed Adaptive Sampling for PINNs (Q6175124) (← links)
- Approximation properties of residual neural networks for fractional differential equations (Q6177827) (← 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 priori error estimate of deep mixed residual method for elliptic PDEs (Q6182315) (← links)
