The following pages link to (Q4938227):
Displaying 34 items.
- Anti-derivatives approximator for enhancing physics-informed neural networks (Q6550163) (← links)
- Compositional sparsity of learnable functions (Q6554713) (← links)
- On the error of approximation by RBF neural networks with two hidden nodes (Q6559089) (← links)
- Wave-packet behaviors of the defocusing nonlinear Schrödinger equation based on the modified physics-informed neural networks (Q6562238) (← links)
- Data-driven models for traffic flow at junctions (Q6562641) (← links)
- Variational temporal convolutional networks for I-FENN thermoelasticity (Q6588274) (← links)
- Nonlinear function-on-scalar regression via functional universal approximation (Q6589276) (← links)
- Inf-sup neural networks for high-dimensional elliptic PDE problems (Q6589859) (← links)
- Least-squares neural network (LSNN) method for linear advection-reaction equation: discontinuity interface (Q6590129) (← links)
- Iterative algorithms for partitioned neural network approximation to partial differential equations (Q6590244) (← links)
- A meshless approach based on fractional interpolation theory and improved neural network bases for solving non-smooth solution of 2D fractional reaction-diffusion equation with distributed order (Q6591004) (← links)
- Can neural networks learn finite elements? (Q6591550) (← links)
- Estimating time-varying reproduction number by deep learning techniques (Q6597354) (← links)
- A pseudoreversible normalizing flow for stochastic dynamical systems with various initial distributions (Q6598493) (← links)
- Neural and spectral operator surrogates: unified construction and expression rate bounds (Q6601288) (← links)
- Recent developments in machine learning methods for stochastic control and games (Q6615618) (← links)
- Deep learning the efficient frontier of convex vector optimization problems (Q6618150) (← links)
- Neural networks can detect model-free static arbitrage strategies (Q6622697) (← links)
- On the omnipresence of spurious local minima in certain neural network training problems (Q6629537) (← links)
- Machine learning algorithm for the Monge-Ampère equation with transport boundary conditions (Q6630933) (← links)
- On the representability of a continuous multivariate function by sums of ridge functions (Q6632950) (← links)
- On the density of translation networks defined on the unit ball (Q6633210) (← links)
- Learning and approximating piecewise smooth functions by deep sigmoid neural networks (Q6634146) (← links)
- Sigmoid functions, multiscale resolution of singularities, and \(hp\)-mesh refinement (Q6636580) (← links)
- RandONets: shallow networks with random projections for learning linear and nonlinear operators (Q6648362) (← links)
- Least-squares neural network (LSNN) method for linear advection-reaction equation: non-constant jumps (Q6648515) (← links)
- Approximation results for gradient flow trained shallow neural networks in \(1d\) (Q6648717) (← links)
- Exponential Sampling Type Kantorovich Max-Product Neural Network Operators (Q6648806) (← links)
- The ADMM-PINNs algorithmic framework for nonsmooth PDE-constrained optimization: a deep learning approach (Q6649881) (← links)
- Weighted variation spaces and approximation by shallow ReLU networks (Q6652573) (← links)
- Fredholm integral equations for function approximation and the training of neural networks (Q6655076) (← links)
- Importance sampling for option pricing with feedforward neural networks (Q6659479) (← links)
- From kernel methods to neural networks: a unifying variational formulation (Q6659493) (← links)
- Approximation of optimal feedback controls for stochastic reaction-diffusion equations (Q6664371) (← links)
