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.

• Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks (Q2222972) (← links)
• Solving electrical impedance tomography with deep learning (Q2223016) (← links)
• Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (Q2223019) (← links)
• Deep multiscale model learning (Q2223279) (← links)
• A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches (Q2237330) (← links)
• Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (Q2237428) (← links)
• Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
• Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations (Q2246361) (← links)
• Conditional physics informed neural networks (Q2247060) (← links)
• A deep energy method for finite deformation hyperelasticity (Q2292258) (← links)
• A machine learning framework for data driven acceleration of computations of differential equations (Q2305115) (← links)
• Variational Monte Carlo -- bridging concepts of machine learning and high-dimensional partial differential equations (Q2305540) (← links)
• An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications (Q2310233) (← links)
• Solving for high-dimensional committor functions using artificial neural networks (Q2319851) (← links)
• Discovering phase field models from image data with the pseudo-spectral physics informed neural networks (Q2667357) (← links)
• Least-squares ReLU neural network (LSNN) method for scalar nonlinear hyperbolic conservation law (Q2668055) (← links)
• A finite element based deep learning solver for parametric PDEs (Q2670366) (← links)
• A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (Q2670380) (← links)
• Adaptive deep neural networks methods for high-dimensional partial differential equations (Q2671349) (← links)
• Towards fast weak adversarial training to solve high dimensional parabolic partial differential equations using XNODE-WAN (Q2671351) (← links)
• On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements (Q2671403) (← links)
• Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← links)
• A deep learning energy method for hyperelasticity and viscoelasticity (Q2671703) (← links)
• Structure preservation for the deep neural network multigrid solver (Q2672194) (← links)
• Surrogate convolutional neural network models for steady computational fluid dynamics simulations (Q2672202) (← links)
• DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
• Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
• Nonlocal kernel network (NKN): a stable and resolution-independent deep neural network (Q2675608) (← links)
• A shallow Ritz method for elliptic problems with singular sources (Q2675616) (← links)
• A discontinuity capturing shallow neural network for elliptic interface problems (Q2675625) (← links)
• Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
• Galerkin neural network approximation of singularly-perturbed elliptic systems (Q2679286) (← links)
• Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights (Q2679332) (← links)
• A deep first-order system least squares method for solving elliptic PDEs (Q2679352) (← links)
• Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator (Q2679950) (← links)
• DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
• Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
• Active learning based sampling for high-dimensional nonlinear partial differential equations (Q2683063) (← links)
• Long-time integration of parametric evolution equations with physics-informed DeepONets (Q2683074) (← links)
• Space-time error estimates for deep neural network approximations for differential equations (Q2683168) (← links)
• ADLGM: an efficient adaptive sampling deep learning Galerkin method (Q2683243) (← links)
• A deep double Ritz method (\(\mathrm{D^2RM}\)) for solving partial differential equations using neural networks (Q2683471) (← links)
• Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
• Data-driven forward and inverse problems for chaotic and hyperchaotic dynamic systems based on two machine learning architectures (Q2688074) (← links)
• Multi-scale fusion network: a new deep learning structure for elliptic interface problems (Q2691986) (← links)
• Deep energy method in topology optimization applications (Q2694685) (← links)
• Control of partial differential equations via physics-informed neural networks (Q2696946) (← links)
• Approximation properties of residual neural networks for Kolmogorov PDEs (Q2697245) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)