Pages that link to "Item:Q4967451"

The following pages link to Solving high-dimensional partial differential equations using deep learning (Q4967451):

Displaying 50 items.

• 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)
• Variational physics informed neural networks: the role of quadratures and test functions (Q2162334) (← links)
• Multilevel Picard approximations of high-dimensional semilinear partial differential equations with locally monotone coefficient functions (Q2165859) (← links)
• Numerical approximation of singular forward-backward SDEs (Q2168288) (← links)
• Deep neural networks based temporal-difference methods for high-dimensional parabolic partial differential equations (Q2168314) (← links)
• Physics-informed distribution transformers via molecular dynamics and deep neural networks (Q2168329) (← links)
• Designing rotationally invariant neural networks from PDEs and variational methods (Q2168880) (← links)
• Fractional Chebyshev deep neural network (FCDNN) for solving differential models (Q2169390) (← links)
• Convergence of deep fictitious play for stochastic differential games (Q2170300) (← links)
• Variational system identification of the partial differential equations governing the physics of pattern-formation: inference under varying fidelity and noise (Q2173625) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• Solving linear parabolic rough partial differential equations (Q2190037) (← links)
• Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks (Q2201474) (← links)
• A numerical approach to Kolmogorov equation in high dimension based on Gaussian analysis (Q2208270) (← links)
• Methods to recover unknown processes in partial differential equations using data (Q2210652) (← links)
• BCR-net: A neural network based on the nonstandard wavelet form (Q2214634) (← links)
• A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations (Q2216499) (← links)
• An efficient numerical algorithm for solving data driven feedback control problems (Q2219806) (← links)
• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (Q2222275) (← links)
• Enforcing constraints for interpolation and extrapolation in generative adversarial networks (Q2222513) (← links)
• Solving many-electron Schrödinger equation using deep neural networks (Q2222630) (← links)
• A mesh-free method for interface problems using the deep learning approach (Q2222664) (← links)
• Solving electrical impedance tomography with deep learning (Q2223016) (← links)
• Convergence of the deep BSDE method for coupled FBSDEs (Q2223111) (← links)
• Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (Q2233120) (← links)
• A modified MSA for stochastic control problems (Q2234329) (← links)
• Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines (Q2236543) (← links)
• Data-driven identification of 2D partial differential equations using extracted physical features (Q2236988) (← links)
• 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)
• On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks (Q2237440) (← links)
• A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
• Deep neural networks for large deformation of photo-thermo-pH responsive cationic gels (Q2240322) (← links)
• Physics informed by deep learning: numerical solutions of modified Korteweg-de Vries equation (Q2244291) (← links)
• Stein variational gradient descent with local approximations (Q2246277) (← links)
• Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
• Learning viscoelasticity models from indirect data using deep neural networks (Q2246355) (← 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)
• Deep learning methods for the computation of vibrational wavefunctions (Q2246978) (← links)
• Variational Monte Carlo -- bridging concepts of machine learning and high-dimensional partial differential equations (Q2305540) (← links)
• An efficient weak Euler-Maruyama type approximation scheme of very high dimensional SDEs by orthogonal random variables (Q2664765) (← links)
• Discovering phase field models from image data with the pseudo-spectral physics informed neural networks (Q2667357) (← 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)
• Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← links)
• Structure preservation for the deep neural network multigrid solver (Q2672194) (← links)
• DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
• A survey of numerical solutions for stochastic control problems: some recent progress (Q2673253) (← links)