Pages that link to "Item:Q1708845"

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

Displaying 50 items.

• Physics informed neural networks for continuum micromechanics (Q2138812) (← links)
• IGA-reuse-NET: a deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization (Q2139715) (← links)
• Dynamic metasurface control using deep reinforcement learning (Q2139890) (← links)
• SEM: a shallow energy method for finite deformation hyperelasticity problems (Q2141514) (← links)
• On sharpness of an error bound for deep ReLU network approximation (Q2143606) (← links)
• Neglected chaos in international stock markets: Bayesian analysis of the joint return-volatility dynamical system (Q2147635) (← links)
• Deep learning for the partially linear Cox model (Q2148978) (← links)
• Numerical bifurcation analysis of PDEs from lattice Boltzmann model simulations: a parsimonious machine learning approach (Q2149520) (← links)
• A distributed FDI cyber-attack detection in discrete-time nonlinear multi-agent systems using neural networks (Q2157847) (← links)
• Approximation properties of deep ReLU CNNs (Q2157922) (← links)
• Lagrangian dual framework for conservative neural network solutions of kinetic equations (Q2158858) (← links)
• Rheological properties of SWCNT/EG mixture by a new developed optimization approach of LS-support vector regression according to empirical data (Q2159657) (← links)
• Robust weighted least squares support vector regression algorithm to estimate the nanofluid thermal properties of water/graphene oxide-silicon carbide mixture (Q2159691) (← links)
• ReLU deep neural networks from the hierarchical basis perspective (Q2159911) (← links)
• Insolvency modeling with generalized entropy cost function in neural networks (Q2160033) (← links)
• Deep reinforcement learning of viscous incompressible flow (Q2162036) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer (Q2164096) (← links)
• Estimate the shear rate \& apparent viscosity of multi-phased non-Newtonian hybrid nanofluids via new developed support vector machine method coupled with sensitivity analysis (Q2164117) (← links)
• Autoencoders reloaded (Q2165369) (← links)
• Sparse Bayesian deep learning for dynamic system identification (Q2165994) (← links)
• Entanglement quantification from collective measurements processed by machine learning (Q2166458) (← 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)
• Fractional Chebyshev deep neural network (FCDNN) for solving differential models (Q2169390) (← links)
• Deep learning solver for solving advection-diffusion equation in comparison to finite difference methods (Q2170855) (← links)
• HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations (Q2172562) (← links)
• Discovering a universal variable-order fractional model for turbulent Couette flow using a physics-informed neural network (Q2173500) (← links)
• An interval uncertainty analysis method for structural response bounds using feedforward neural network differentiation (Q2174709) (← links)
• A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling (Q2175074) (← links)
• Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations (Q2176735) (← links)
• On a high-dimensional model representation method based on copulas (Q2178128) (← links)
• Approximation-based fixed-time adaptive tracking control for a class of uncertain nonlinear pure-feedback systems (Q2179154) (← links)
• Standard representation and unified stability analysis for dynamic artificial neural network models (Q2179309) (← links)
• Exploiting layerwise convexity of rectifier networks with sign constrained weights (Q2181101) (← links)
• Optimal approximation of piecewise smooth functions using deep ReLU neural networks (Q2182898) (← links)
• Echo state networks are universal (Q2182904) (← links)
• The capacity of feedforward neural networks (Q2183684) (← links)
• An interval uncertain optimization method using back-propagation neural network differentiation (Q2184463) (← links)
• An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture (Q2184471) (← links)
• Nonlinear approximation via compositions (Q2185653) (← links)
• Theory of deep convolutional neural networks: downsampling (Q2185717) (← links)
• Universal approximation with quadratic deep networks (Q2185719) (← links)
• Robust min-max optimal control design for systems with uncertain models: a neural dynamic programming approach (Q2185769) (← links)
• Geochemical equilibrium determination using an artificial neural network in compositional reservoir flow simulation (Q2185985) (← links)
• Assessment of end-to-end and sequential data-driven learning for non-intrusive modeling of fluid flows (Q2190672) (← links)
• Generalized hidden-mapping minimax probability machine for the training and reliability learning of several classical intelligent models (Q2195441) (← links)
• Deep neural network approach to forward-inverse problems (Q2197226) (← links)
• End to end communication rate-based adaptive fault tolerant control of multi-agent systems under unreliable interconnections (Q2198210) (← links)
• Cooperative neuro adaptive control of leader following uncertain multi-agent systems with unknown hysteresis and dead-zone (Q2200099) (← links)