Pages that link to "Item:Q1708845"

The following pages link to Multilayer feedforward networks are universal approximators (Q1708845):

Displaying 50 items.

• Neural network models for inflation forecasting: a revisit (Q2086163) (← links)
• Forecasts of GDP growth and inflation under the influence of the Covid-19 pandemic: the case of Vietnam (Q2086226) (← links)
• The construction and approximation of ReLU neural network operators (Q2086452) (← links)
• On the approximation of rough functions with deep neural networks (Q2089012) (← links)
• Solving ordinary differential equations using an optimization technique based on training improved artificial neural networks (Q2099861) (← links)
• Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach (Q2103679) (← links)
• Optimal control of PDEs using physics-informed neural networks (Q2106939) (← links)
• Quantum activation functions for quantum neural networks (Q2107069) (← links)
• Non-homogeneous Poisson process intensity modeling and estimation using measure transport (Q2108510) (← links)
• Error bounds for ReLU networks with depth and width parameters (Q2111556) (← links)
• On stability and regularization for data-driven solution of parabolic inverse source problems (Q2112451) (← links)
• Neural eikonal solver: improving accuracy of physics-informed neural networks for solving eikonal equation in case of caustics (Q2112483) (← links)
• Nonlinear input feature reduction for data-based physical modeling (Q2112546) (← links)
• The nonlinear wave solutions and parameters discovery of the Lakshmanan-Porsezian-Daniel based on deep learning (Q2113140) (← links)
• Deep learning Gauss-Manin connections (Q2113261) (← links)
• Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers (Q2115516) (← links)
• Data-driven predictions of the Lorenz system (Q2115546) (← links)
• A new mesh smoothing method based on a neural network (Q2115586) (← links)
• A theoretical analysis of deep neural networks and parametric PDEs (Q2117329) (← links)
• Nonlinear approximation and (deep) ReLU networks (Q2117331) (← links)
• Neural network identifiability for a family of sigmoidal nonlinearities (Q2117333) (← links)
• Approximation spaces of deep neural networks (Q2117336) (← links)
• Universal approximations of invariant maps by neural networks (Q2117338) (← links)
• Exponential ReLU DNN expression of holomorphic maps in high dimension (Q2117341) (← links)
• Best \(k\)-layer neural network approximations (Q2117342) (← links)
• A tutorial on optimal control and reinforcement learning methods for quantum technologies (Q2119346) (← links)
• Chaotic signals inside some tick-by-tick financial time series (Q2120710) (← links)
• A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks (Q2122243) (← links)
• An efficient neural network method with plane wave activation functions for solving Helmholtz equation (Q2122592) (← links)
• Adaptive two-layer ReLU neural network. I: Best least-squares approximation (Q2122629) (← links)
• Adaptive two-layer ReLU neural network. II: Ritz approximation to elliptic PDEs (Q2122635) (← links)
• Interpolation and approximation via momentum ResNets and neural ODEs (Q2124500) (← links)
• Trend to equilibrium for the kinetic Fokker-Planck equation via the neural network approach (Q2125428) (← links)
• A derivative-free method for solving elliptic partial differential equations with deep neural networks (Q2125435) (← links)
• Structure-preserving neural networks (Q2127014) (← links)
• PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
• Solving the linear transport equation by a deep neural network approach (Q2129138) (← links)
• Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows (Q2129550) (← links)
• SelectNet: self-paced learning for high-dimensional partial differential equations (Q2131038) (← links)
• DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks (Q2131084) (← links)
• A statistical model of neural network learning via the Cramer-Rao lower bound (Q2132021) (← links)
• Metric entropy limits on recurrent neural network learning of linear dynamical systems (Q2134114) (← links)
• Machine learning for fluid flow reconstruction from limited measurements (Q2134510) (← links)
• The mixed deep energy method for resolving concentration features in finite strain hyperelasticity (Q2134762) (← 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)
• On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (Q2136745) (← links)
• RotEqNet: rotation-equivariant network for fluid systems with symmetric high-order tensors (Q2138017) (← links)
• Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks (Q2138793) (← links)
• A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (Q2138799) (← links)