Pages that link to "Item:Q2638735"

The following pages link to Neural algorithm for solving differential equations (Q2638735):

Displaying 50 items.

• LS-SVM approximate solution for affine nonlinear systems with partially unknown functions (Q380605) (← links)
• Inverse problem with respect to domain and artificial neural network algorithm for the solution (Q410280) (← links)
• Weak adversarial networks for high-dimensional partial differential equations (Q777606) (← links)
• Overcoming the curse of dimensionality for some Hamilton-Jacobi partial differential equations via neural network architectures (Q783094) (← links)
• Numerical solution for high order differential equations using a hybrid neural network-optimization method (Q864765) (← links)
• The numerical solution of linear ordinary differential equations by feedforward neural networks (Q1334712) (← links)
• Solution of nonlinear ordinary differential equations by feedforward neural networks (Q1344634) (← links)
• DGM: a deep learning algorithm for solving partial differential equations (Q2002333) (← links)
• Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization (Q2021855) (← links)
• Hierarchical deep-learning neural networks: finite elements and beyond (Q2033626) (← links)
• A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations (Q2034897) (← links)
• Numerical methods for solving fuzzy equations: a survey (Q2035392) (← links)
• Interpretable machine learning: fundamental principles and 10 grand challenges (Q2074414) (← links)
• CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
• A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
• Lookback option pricing under the double Heston model using a deep learning algorithm (Q2099529) (← links)
• Solving ordinary differential equations using an optimization technique based on training improved artificial neural networks (Q2099861) (← links)
• Uniform convergence guarantees for the deep Ritz method for nonlinear problems (Q2110466) (← links)
• GINNs: graph-informed neural networks for multiscale physics (Q2120776) (← links)
• Using deep learning to extend the range of air pollution monitoring and forecasting (Q2123348) (← links)
• On some neural network architectures that can represent viscosity solutions of certain high dimensional Hamilton-Jacobi partial differential equations (Q2123971) (← links)
• Structure probing neural network deflation (Q2124019) (← links)
• PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
• PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries (Q2128373) (← links)
• SelectNet: self-paced learning for high-dimensional partial differential equations (Q2131038) (← links)
• Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains (Q2222287) (← links)
• A mesh-free method for interface problems using the deep learning approach (Q2222664) (← links)
• Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations (Q2327815) (← 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)
• DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
• A deep first-order system least squares method for solving elliptic PDEs (Q2679352) (← links)
• Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes (Q2680327) (← 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)
• Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
• An efficient numerical method to solve ordinary differential equations using Fibonacci neural networks (Q2686539) (← links)
• opPINN: physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation (Q2689626) (← links)
• Solving Dirichlet boundary problems for ODEs via swarm intelligence (Q2690417) (← links)
• Numerical solution of Helmholtz equation by the modified Hopfield finite difference techniques (Q3629567) (← links)
• (Q4220002) (← links)
• Path-Dependent Deep Galerkin Method: A Neural Network Approach to Solve Path-Dependent Partial Differential Equations (Q4958400) (← links)
• (Q5019878) (← links)
• Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
• On a multilevel Levenberg–Marquardt method for the training of artificial neural networks and its application to the solution of partial differential equations (Q5038185) (← links)
• PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations (Q5045670) (← links)
• A New Artificial Neural Network Method for Solving Schrödinger Equations on Unbounded Domains (Q5045673) (← links)