Pages that link to "Item:Q4967451"

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

Displaying 50 items.

• Solving PDEs on unknown manifolds with machine learning (Q6499004) (← links)
• Data-driven modeling of partially observed biological systems (Q6537200) (← links)
• PDNNs: the parallel deep neural networks for the Navier-Stokes equations coupled with heat equation (Q6537439) (← links)
• Solving the regularized Schamel equation by the singular planar dynamical system method and the deep learning method (Q6538831) (← links)
• Solving the non-local Fokker-Planck equations by deep learning (Q6551384) (← links)
• Meshless physics-informed deep learning method for three-dimensional solid mechanics (Q6554056) (← links)
• Numerical algorithm with fifth-order accuracy for axisymmetric Laplace equation with linear boundary value problem (Q6556888) (← links)
• Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations (Q6557699) (← links)
• On the locality of local neural operator in learning fluid dynamics (Q6557796) (← links)
• Numerical solutions of boundary problems in partial differential equations: a deep learning framework with Green's function (Q6560698) (← links)
• An optimal control method to compute the most likely transition path for stochastic dynamical systems with jumps (Q6563609) (← links)
• Gabor-filtered Fourier neural operator for solving partial differential equations (Q6566939) (← links)
• A batch process for high dimensional imputation (Q6567425) (← links)
• Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons (Q6569679) (← links)
• Neuro-PINN: a hybrid framework for efficient nonlinear projection equation solutions (Q6569923) (← links)
• The Calderón's problem via DeepONets (Q6570540) (← links)
• Feynman-Kac equation for Brownian non-Gaussian polymer diffusion (Q6571592) (← links)
• Non-diffusive neural network method for hyperbolic conservation laws (Q6572177) (← links)
• Lax-Oleinik-type formulas and efficient algorithms for certain high-dimensional optimal control problems (Q6575313) (← links)
• Optimization of random feature method in the high-precision regime (Q6575315) (← links)
• Temporal difference learning for high-dimensional PIDEs with jumps (Q6575343) (← links)
• A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations (Q6584818) (← links)
• A causality-DeepONet for causal responses of linear dynamical systems (Q6584819) (← links)
• Energetic variational neural network discretizations of gradient flows (Q6585315) (← links)
• Adaptive sampling points based multi-scale residual network for solving partial differential equations (Q6585372) (← links)
• A deep learning method for solving multi-dimensional coupled forward-backward doubly SDEs (Q6585377) (← links)
• One-shot learning of surrogates in PDE-constrained optimization under uncertainty (Q6587616) (← links)
• Natural model reduction for kinetic equations (Q6588181) (← links)
• Deep JKO: time-implicit particle methods for general nonlinear gradient flows (Q6589858) (← links)
• Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning (Q6598418) (← links)
• A neural network approach for stochastic optimal control (Q6598497) (← links)
• Solving optimal control problems governed by nonlinear PDEs using a multilevel method based on an artificial neural network (Q6602281) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← links)
• Two-time scale reinforcement learning and applications to production planning (Q6611543) (← links)
• Recent developments in machine learning methods for stochastic control and games (Q6615618) (← links)
• Numerical solution of Poisson partial differential equation in high dimension using two-layer neural networks (Q6622387) (← links)
• Multigrid-augmented deep learning preconditioners for the Helmholtz equation using compact implicit layers (Q6623680) (← links)
• Nonlinear embeddings for conserving Hamiltonians and other quantities with neural Galerkin schemes (Q6623695) (← links)
• A multilinear HJB-POD method for the optimal control of PDEs on a tree structure (Q6629218) (← links)
• Unsupervised random quantum networks for PDEs (Q6629259) (← links)
• Approximation and generalization of DeepONets for learning operators arising from a class of singularly perturbed problems (Q6630935) (← links)
• Consistent smooth approximation of feedback laws for infinite horizon control problems with non-smooth value functions (Q6632963) (← links)
• PDE generalization of in-context operator networks: a study on 1D scalar nonlinear conservation laws (Q6639294) (← links)
• Generative downscaling of PDE solvers with physics-guided diffusion models (Q6639511) (← links)
• Deep adaptive sampling for surrogate modeling without labeled data (Q6639518) (← links)
• Physics-aware neural implicit solvers for multiscale, parametric PDEs with applications in heterogeneous media (Q6641874) (← links)
• Parameter identification by deep learning of a material model for granular media (Q6643277) (← links)
• PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
• Vanilla feedforward neural networks as a discretization of dynamical systems (Q6645925) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations (Q6645961) (← links)