Pages that link to "Item:Q4294523"
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The following pages link to Neural‐network‐based approximations for solving partial differential equations (Q4294523):
Displaying 50 items.
- Stationary Density Estimation of Itô Diffusions Using Deep Learning (Q5886225) (← links)
- Deep learning neural networks for the third-order nonlinear Schrödinger equation: bright solitons, breathers, and rogue waves (Q6046358) (← links)
- Solving second-order nonlinear evolution partial differential equations using deep learning (Q6048188) (← links)
- Application of the dynamical system method and the deep learning method to solve the new (3+1)-dimensional fractional modified Benjamin-Bona-Mahony equation (Q6059936) (← links)
- Physics-Informed Neural Networks for Solving Dynamic Two-Phase Interface Problems (Q6068803) (← links)
- VC-PINN: variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient (Q6069931) (← links)
- The deep minimizing movement scheme (Q6087937) (← links)
- A dimension-augmented physics-informed neural network (DaPINN) with high level accuracy and efficiency (Q6095102) (← links)
- FluxNet: a physics-informed learning-based Riemann solver for transcritical flows with non-ideal thermodynamics (Q6097610) (← links)
- Data-driven vortex solitons and parameter discovery of 2D generalized nonlinear Schrödinger equations with a \(\mathcal{PT}\)-symmetric optical lattice (Q6103701) (← links)
- Learning high frequency data via the coupled frequency predictor-corrector triangular DNN (Q6104304) (← links)
- Comparison of neural closure models for discretised PDEs (Q6104834) (← links)
- A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
- Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning (Q6108164) (← links)
- Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
- A Chebyshev Polynomial Neural Network Solver for Boundary Value Problems of Elliptic Equations (Q6110098) (← links)
- Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs) (Q6111307) (← links)
- Higher-order error estimates for physics-informed neural networks approximating the primitive equations (Q6114171) (← links)
- Neural Galerkin schemes with active learning for high-dimensional evolution equations (Q6117685) (← links)
- HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions (Q6119293) (← links)
- An extreme learning machine-based method for computational PDEs in higher dimensions (Q6120177) (← links)
- Transferable neural networks for partial differential equations (Q6123346) (← links)
- Loss-attentional physics-informed neural networks (Q6126561) (← links)
- A conservative hybrid deep learning method for Maxwell-Ampère-Nernst-Planck equations (Q6126572) (← links)
- Gradient-Based Differential Neural-Solution to Time-Dependent Nonlinear Optimization (Q6137551) (← links)
- Error analysis of deep Ritz methods for elliptic equations (Q6145797) (← links)
- Connections between numerical algorithms for PDEs and neural networks (Q6156049) (← links)
- Boundary-safe PINNs extension: application to non-linear parabolic PDEs in counterparty credit risk (Q6157931) (← links)
- Asymptotic-preserving neural networks for multiscale time-dependent linear transport equations (Q6158979) (← links)
- Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine (Q6159015) (← links)
- A symmetry group based supervised learning method for solving partial differential equations (Q6171229) (← links)
- Least-squares neural network (LSNN) method for scalar nonlinear hyperbolic conservation laws: discrete divergence operator (Q6175199) (← links)
- Solving groundwater flow equation using physics-informed neural networks (Q6176691) (← links)
- An artificial neural network approach to identify the parameter in a nonlinear subdiffusion model (Q6177840) (← links)
- A Neural Network Approach for Homogenization of Multiscale Problems (Q6178099) (← links)
- Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← links)
- On the spectral bias of coupled frequency predictor-corrector triangular DNN: the convergence analysis (Q6179933) (← links)
- A nonlinear-manifold reduced-order model and operator learning for partial differential equations with sharp solution gradients (Q6185246) (← links)
- Gradient-enhanced physics-informed neural networks based on transfer learning for inverse problems of the variable coefficient differential equations (Q6191522) (← links)
- Neural Control of Parametric Solutions for High-Dimensional Evolution PDEs (Q6194975) (← links)
- wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (Q6197777) (← links)
- Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent? (Q6198233) (← links)
- Physics-informed ConvNet: learning physical field from a shallow neural network (Q6199712) (← links)
- CCGnet: a deep learning approach to predict Nash equilibrium of chance-constrained games (Q6492614) (← links)
- Zero coordinate shift: whetted automatic differentiation for physics-informed operator learning (Q6497254) (← links)
- Solving the regularized Schamel equation by the singular planar dynamical system method and the deep learning method (Q6538831) (← links)
- CEENs: causality-enforced evolutional networks for solving time-dependent partial differential equations (Q6557798) (← links)
- A dynamical neural network approach for distributionally robust chance-constrained Markov decision process (Q6564772) (← links)
- Learning scattering waves via coupling physics-informed neural networks and their convergence analysis (Q6567300) (← links)
- Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons (Q6569679) (← links)
