Pages that link to "Item:Q2002333"

The following pages link to DGM: a deep learning algorithm for solving partial differential equations (Q2002333):

Displaying 50 items.

• A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches (Q2237330) (← links)
• Learning nonlocal constitutive models with neural networks (Q2237430) (← links)
• Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (Q2237458) (← 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)
• Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients (Q2246423) (← links)
• Optimal market-making strategies under synchronised order arrivals with deep neural networks (Q2246653) (← links)
• Artificial neural network approximations of Cauchy inverse problem for linear PDEs (Q2247118) (← links)
• A deep energy method for finite deformation hyperelasticity (Q2292258) (← links)
• A modern retrospective on probabilistic numerics (Q2302460) (← links)
• Machine learning for semi linear PDEs (Q2316193) (← links)
• Solving for high-dimensional committor functions using artificial neural networks (Q2319851) (← links)
• A multiscale neural network based on hierarchical nested bases (Q2319969) (← links)
• Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations (Q2327815) (← links)
• The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling (Q2333058) (← links)
• An exploratory study on machine learning to couple numerical solutions of partial differential equations (Q2656809) (← links)
• Linear-quadratic stochastic delayed control and deep learning resolution (Q2664898) (← links)
• A hybrid MGA-MSGD ANN training approach for approximate solution of linear elliptic PDEs (Q2666253) (← links)
• Least-squares ReLU neural network (LSNN) method for scalar nonlinear hyperbolic conservation law (Q2668055) (← links)
• Accelerating high order discontinuous Galerkin solvers using neural networks: 1D Burgers' equation (Q2670077) (← links)
• A finite element based deep learning solver for parametric PDEs (Q2670366) (← 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)
• Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (Q2671386) (← 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)
• DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
• Numerical solution of the Fokker-Planck equation using physics-based mixture models (Q2674128) (← links)
• The deep learning Galerkin method for the general Stokes equations (Q2674271) (← 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)
• 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)
• SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
• Modeling systems with machine learning based differential equations (Q2680007) (← links)
• Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes (Q2680327) (← links)
• DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
• Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
• Inverse stochastic optimal controls (Q2681368) (← links)
• CPINNs: a coupled physics-informed neural networks for the closed-loop geothermal system (Q2682678) (← links)
• Active learning based sampling for high-dimensional nonlinear partial differential equations (Q2683063) (← 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)
• Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
• A deep Fourier residual method for solving PDEs using neural networks (Q2683430) (← 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)
• Stochastic projection based approach for gradient free physics informed learning (Q2686876) (← links)
• QBoost for regression problems: solving partial differential equations (Q2687371) (← links)