Pages that link to "Item:Q4294523"

The following pages link to Neural‐network‐based approximations for solving partial differential equations (Q4294523):

Displaying 50 items.

• Automated design parameter selection for neural networks solving coupled partial differential equations with discontinuities (Q388540) (← links)
• Buckling analysis of a beam-column using multilayer perceptron neural network technique (Q398291) (← links)
• Solving initial-boundary value problems for systems of partial differential equations using neural networks and optimization techniques (Q468201) (← links)
• A feed-forward neural network for solving Stokes problem (Q645014) (← links)
• Approximation solution of fractional partial differential equations by neural networks (Q666395) (← links)
• A meshless method using the Takagi-Sugeno fuzzy model (Q695500) (← links)
• 3D nonparametric neural identification (Q763177) (← links)
• Weak adversarial networks for high-dimensional partial differential equations (Q777606) (← links)
• Neural network algorithm based on Legendre improved extreme learning machine for solving elliptic partial differential equations (Q780292) (← links)
• Overcoming the curse of dimensionality for some Hamilton-Jacobi partial differential equations via neural network architectures (Q783094) (← links)
• Solving forward and inverse problems of the logarithmic nonlinear Schrödinger equation with \(\mathcal{PT}\)-symmetric harmonic potential via deep learning (Q822569) (← links)
• Numerical solution for high order differential equations using a hybrid neural network-optimization method (Q864765) (← links)
• Establishing criteria to ensure successful feedforward artificial neural network modelling of mechanical systems (Q969828) (← links)
• Neural computation for robust approximate pole assignment (Q1305913) (← links)
• Mesh-free radial basis function network methods with domain decomposition for approximation of functions and numerical solution of Poisson's equations (Q1604037) (← links)
• Solving 2D and 3D Poisson equations and biharmonic equations by the Haar wavelet method (Q1930718) (← links)
• Approximation of functions and their derivatives: A neural network implementation with applications (Q1960876) (← links)
• Traveling wave solutions of partial differential equations via neural networks (Q1983171) (← links)
• Analytical solutions of boundary values problem of 2D and 3D Poisson and biharmonic equations by homotopy decomposition method (Q2015431) (← links)
• PPINN: parareal physics-informed neural network for time-dependent PDEs (Q2020276) (← links)
• Numerical methods for solving fuzzy equations: a survey (Q2035392) (← links)
• Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning (Q2077801) (← links)
• Data-driven discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes (Q2081273) (← links)
• Error-correcting neural networks for semi-Lagrangian advection in the level-set method (Q2088344) (← links)
• The distortion of the Peregrine soliton under the perturbation in initial condition (Q2093720) (← links)
• Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545) (← links)
• Efficient coupled deep neural networks for the time-dependent coupled Stokes-Darcy problems (Q2096255) (← links)
• Solving partial differential equation based on extreme learning machine (Q2104376) (← links)
• Optimal control of PDEs using physics-informed neural networks (Q2106939) (← links)
• Uniform convergence guarantees for the deep Ritz method for nonlinear problems (Q2110466) (← links)
• Self-adaptive physics-informed neural networks (Q2112437) (← links)
• Two neural-network-based methods for solving elliptic obstacle problems (Q2112890) (← links)
• Adaptive two-layer ReLU neural network. I: Best least-squares approximation (Q2122629) (← 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)
• Deep least-squares methods: an unsupervised learning-based numerical method for solving elliptic PDEs (Q2125011) (← links)
• Int-Deep: a deep learning initialized iterative method for nonlinear problems (Q2125440) (← links)
• SelectNet: self-paced learning for high-dimensional partial differential equations (Q2131038) (← links)
• Least-squares ReLU neural network (LSNN) method for linear advection-reaction equation (Q2132582) (← links)
• Using neural networks to accelerate the solution of the Boltzmann equation (Q2132591) (← links)
• Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation (Q2133495) (← links)
• 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)
• On quadrature rules for solving partial differential equations using neural networks (Q2138756) (← links)
• Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Q2138842) (← 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)
• An investigation of approximate solutions for second order ordinary differential equations using sigmoid-weighted neural networks (Q2144766) (← links)
• Polynomial neural forms using feedforward neural networks for solving differential equations (Q2148710) (← links)