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.

• Self-adaptive deep neural network: numerical approximation to functions and PDEs (Q2133768) (← links)
• A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder (Q2134764) (← links)
• DeLISA: deep learning based iteration scheme approximation for solving PDEs (Q2134800) (← links)
• Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244) (← links)
• Adaptive deep density approximation for Fokker-Planck equations (Q2135831) (← links)
• When and why PINNs fail to train: a neural tangent kernel perspective (Q2136450) (← links)
• On quadrature rules for solving partial differential equations using neural networks (Q2138756) (← links)
• Neural networks enforcing physical symmetries in nonlinear dynamical lattices: the case example of the Ablowitz-Ladik model (Q2140106) (← links)
• A mesh-free method using piecewise deep neural network for elliptic interface problems (Q2141617) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints (Q2142219) (← links)
• Solving Fredholm integral equations using deep learning (Q2144736) (← links)
• Extensions of the deep Galerkin method (Q2148058) (← links)
• Computing the invariant distribution of randomly perturbed dynamical systems using deep learning (Q2149015) (← links)
• A decision-making machine learning approach in Hermite spectral approximations of partial differential equations (Q2149019) (← links)
• Error estimates for deep learning methods in fluid dynamics (Q2149063) (← links)
• Discovery of subdiffusion problem with noisy data via deep learning (Q2149161) (← links)
• Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms (Q2152480) (← links)
• Solving elliptic equations with Brownian motion: bias reduction and temporal difference learning (Q2157396) (← links)
• Solving multiscale steady radiative transfer equation using neural networks with uniform stability (Q2157930) (← links)
• Lagrangian dual framework for conservative neural network solutions of kinetic equations (Q2158858) (← links)
• Numerical approximation of partial differential equations by a variable projection method with artificial neural networks (Q2160472) (← links)
• Randomized Newton's method for solving differential equations based on the neural network discretization (Q2161555) (← links)
• The deep parametric PDE method and applications to option pricing (Q2161843) (← links)
• Deep reinforcement learning of viscous incompressible flow (Q2162036) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities (Q2162115) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Modelling spatiotemporal dynamics from Earth observation data with neural differential equations (Q2163266) (← links)
• Multilevel Picard approximations of high-dimensional semilinear partial differential equations with locally monotone coefficient functions (Q2165859) (← links)
• Solving flows of dynamical systems by deep neural networks and a novel deep learning algorithm (Q2168118) (← links)
• Deep neural networks based temporal-difference methods for high-dimensional parabolic partial differential equations (Q2168314) (← links)
• Fractional Chebyshev deep neural network (FCDNN) for solving differential models (Q2169390) (← links)
• Convergence of deep fictitious play for stochastic differential games (Q2170300) (← links)
• HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations (Q2172562) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery (Q2179220) (← links)
• Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks (Q2201474) (← links)
• Linearized implicit methods based on a single-layer neural network: application to Keller-Segel models (Q2204551) (← links)
• Neural-net-induced Gaussian process regression for function approximation and PDE solution (Q2214653) (← links)
• A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations (Q2216499) (← links)
• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (Q2222275) (← links)
• ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains (Q2222287) (← links)
• Enforcing constraints for interpolation and extrapolation in generative adversarial networks (Q2222513) (← links)
• A mesh-free method for interface problems using the deep learning approach (Q2222664) (← links)
• Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks (Q2222972) (← links)
• Coercing machine learning to output physically accurate results (Q2223280) (← links)
• Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (Q2233120) (← links)
• Lifetime ruin under high-water mark fees and drift uncertainty (Q2234305) (← links)
• Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets (Q2236928) (← links)
• Data-driven identification of 2D partial differential equations using extracted physical features (Q2236988) (← links)