The following pages link to (Q4938227):
Displaying 50 items.
- A class of dimension-free metrics for the convergence of empirical measures (Q6072907) (← links)
- Neural network interpolation operators optimized by Lagrange polynomial (Q6077041) (← links)
- Applications of limiters, neural networks and polynomial annihilation in higher-order FD/FV schemes (Q6077300) (← links)
- A survey on modern trainable activation functions (Q6078705) (← links)
- A novel fully adaptive neural network modeling and implementation using colored Petri nets (Q6080673) (← links)
- A cusp-capturing PINN for elliptic interface problems (Q6095097) (← links)
- Time discretization in the solution of parabolic PDEs with ANNs (Q6096361) (← links)
- Deep Ritz method with adaptive quadrature for linear elasticity (Q6096475) (← links)
- Towards Lower Bounds on the Depth of ReLU Neural Networks (Q6100606) (← links)
- Universal regular conditional distributions via probabilistic transformers (Q6101232) (← links)
- Accuracy and architecture studies of residual neural network method for ordinary differential equations (Q6101548) (← links)
- DeepBHCP: deep neural network algorithm for solving backward heat conduction problems (Q6102002) (← links)
- Data-driven vortex solitons and parameter discovery of 2D generalized nonlinear Schrödinger equations with a \(\mathcal{PT}\)-symmetric optical lattice (Q6103701) (← links)
- Approximation by sums of shifts and dilations of a single function and neural networks (Q6105856) (← links)
- Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation (Q6107984) (← links)
- Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks (Q6109143) (← links)
- Learning sparse and smooth functions by deep sigmoid nets (Q6109261) (← links)
- Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
- Heaviside function as an activation function (Q6112992) (← links)
- Neural ODE Control for Classification, Approximation, and Transport (Q6115450) (← links)
- Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance (Q6116144) (← links)
- Noncompact uniform universal approximation (Q6121956) (← links)
- Higher Order Orthogonal Polynomials as Activation Functions in Artificial Neural Networks (Q6136053) (← links)
- Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class (Q6137593) (← links)
- Neural network interpolation operators of multivariate functions (Q6137791) (← links)
- A Variational Neural Network Approach for Glacier Modelling with Nonlinear Rheology (Q6143617) (← links)
- Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons (Q6143642) (← links)
- Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning (Q6143823) (← links)
- Error bounds for approximations using multichannel deep convolutional neural networks with downsampling (Q6155792) (← links)
- Lower bounds for artificial neural network approximations: a proof that shallow neural networks fail to overcome the curse of dimensionality (Q6155895) (← links)
- ReLU neural network Galerkin BEM (Q6159305) (← links)
- Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data (Q6167049) (← links)
- Deep neural network based adaptive learning for switched systems (Q6172098) (← links)
- Dynamical Systems–Based Neural Networks (Q6181900) (← links)
- Fractional type multivariate neural network operators (Q6183031) (← links)
- Lu decomposition and Toeplitz decomposition of a neural network (Q6185692) (← links)
- Approximation of curve-based sleeve functions in high dimensions (Q6185807) (← links)
- Approximation error of single hidden layer neural networks with fixed weights (Q6195340) (← links)
- Reinforcement learning with dynamic convex risk measures (Q6196296) (← links)
- Designing universal causal deep learning models: The geometric (Hyper)transformer (Q6196301) (← links)
- Operator approximation of the wave equation based on deep learning of Green's function (Q6202600) (← links)
- Approximation in shift-invariant spaces with deep ReLU neural networks (Q6341347) (← links)
- Integral representations of shallow neural network with Rectified Power Unit activation function (Q6386309) (← links)
- PDE-READ: human-readable partial differential equation discovery using deep learning (Q6488684) (← links)
- A fast and accurate domain decomposition nonlinear manifold reduced order model (Q6497183) (← links)
- Artificial neural networks with uniform norm-based loss functions (Q6500165) (← links)
- An adaptive discrete physics-informed neural network method for solving the Cahn-Hilliard equation (Q6539904) (← links)
- A new meshless method of solving the distributed-order time-fractional mobile-immobile equations (Q6540949) (← links)
- Weight normalized deep neural networks (Q6541748) (← links)
- Expressive power of ReLU and step networks under floating-point operations (Q6543653) (← links)
