Pages that link to "Item:Q1708845"

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

Displaying 50 items.

• Comparative study of neural networks for dynamic nonlinear systems identification (Q2001119) (← links)
• Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (Q2002273) (← links)
• Selection dynamics for deep neural networks (Q2003969) (← links)
• Metamodeling of aircraft infrared signature dispersion (Q2006889) (← links)
• Using machine learning to improve cylindrical algebraic decomposition (Q2009221) (← links)
• A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks (Q2020834) (← links)
• A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network (Q2020855) (← links)
• SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (Q2020876) (← links)
• A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model (Q2020945) (← links)
• Deep learned finite elements (Q2021024) (← links)
• Universal machine learning for topology optimization (Q2022034) (← links)
• A review on computational intelligence for identification of nonlinear dynamical systems (Q2023111) (← links)
• An enhanced multi-objective evolutionary optimization algorithm with inverse model (Q2023266) (← links)
• Efficient approximation of solutions of parametric linear transport equations by ReLU DNNs (Q2026114) (← links)
• Topological properties of the set of functions generated by neural networks of fixed size (Q2031060) (← links)
• Discretization and machine learning approximation of BSDEs with a constraint on the gains-process (Q2031302) (← links)
• Hierarchical deep-learning neural networks: finite elements and beyond (Q2033626) (← links)
• Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning (Q2035195) (← links)
• Approximation by exponential sampling type neural network operators (Q2037365) (← links)
• Computing Lyapunov functions using deep neural networks (Q2043422) (← links)
• The universal approximation property. Characterization, construction, representation, and existence (Q2043428) (← links)
• Solving the chaos model-data paradox in the cryptocurrency market (Q2045933) (← links)
• Interpolation of sparse high-dimensional data (Q2048831) (← links)
• Error bounds for deep ReLU networks using the Kolmogorov-Arnold superposition theorem (Q2055036) (← links)
• Fast generalization error bound of deep learning without scale invariance of activation functions (Q2055056) (← links)
• SSN: learning sparse switchable normalization via SparsestMax (Q2056134) (← links)
• Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness (Q2057701) (← links)
• \(k\)-hop graph neural networks (Q2057706) (← links)
• A pruning feedforward small-world neural network based on Katz centrality for nonlinear system modeling (Q2057709) (← links)
• Two-hidden-layer feed-forward networks are universal approximators: a constructive approach (Q2057712) (← links)
• Sympnets: intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems (Q2057752) (← links)
• An analytic layer-wise deep learning framework with applications to robotics (Q2059394) (← links)
• On the stability properties of gated recurrent units neural networks (Q2059489) (← links)
• Pricing equity-linked life insurance contracts with multiple risk factors by neural networks (Q2059681) (← links)
• Explainable neural network for pricing and universal static hedging of contingent claims (Q2060236) (← links)
• High-dimensional distribution generation through deep neural networks (Q2062235) (← links)
• Optimal approximation rate of ReLU networks in terms of width and depth (Q2065073) (← links)
• An individual claims reserving model for reported claims (Q2066783) (← links)
• Optimally weighted loss functions for solving PDEs with neural networks (Q2068635) (← links)
• Reinforcement learning and stochastic optimisation (Q2072112) (← links)
• Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• HiDeNN-TD: reduced-order hierarchical deep learning neural networks (Q2072507) (← links)
• Fitting small piece-wise linear neural network models to interpolate data sets (Q2072583) (← links)
• Normalization effects on shallow neural networks and related asymptotic expansions (Q2072629) (← links)
• A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
• Rates of approximation by neural network interpolation operators (Q2073064) (← links)
• Deep reinforcement learning for inventory control: a roadmap (Q2076812) (← links)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics (Q2083129) (← links)
• Data-driven solutions and parameter discovery of the Sasa-Satsuma equation via the physics-informed neural networks method (Q2083739) (← links)