Pages that link to "Item:Q5230662"

The following pages link to fPINNs: Fractional Physics-Informed Neural Networks (Q5230662):

Displaying 50 items.

• VC-PINN: variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient (Q6069931) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Deep learning phase‐field model for brittle fractures (Q6071412) (← links)
• Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations (Q6078492) (← links)
• Adaptive deep density approximation for fractional Fokker-Planck equations (Q6087826) (← links)
• A fractional model for anomalous diffusion with increased variability: Analysis, algorithms and applications to interface problems (Q6090390) (← links)
• Fully probabilistic deep models for forward and inverse problems in parametric PDEs (Q6095115) (← links)
• Complex dynamics on the one-dimensional quantum droplets via time piecewise PINNs (Q6096531) (← links)
• Deep learning for thermal plasma simulation: solving 1-D arc model as an example (Q6097959) (← links)
• Radial basis function neural network (RBFNN) approximation of Cauchy inverse problems of the Laplace equation (Q6103633) (← links)
• Pre-training strategy for solving evolution equations based on physics-informed neural networks (Q6107095) (← links)
• MGIC: Multigrid-in-Channels Neural Network Architectures (Q6108155) (← links)
• An Asymptotically Compatible Coupling Formulation for Nonlocal Interface Problems with Jumps (Q6108168) (← links)
• Deep learning discrete calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research (Q6109277) (← links)
• HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions (Q6119293) (← links)
• A new method for solving nonlinear partial differential equations based on liquid time-constant networks (Q6130983) (← links)
• Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems (Q6130985) (← links)
• Physics-informed neural networks with two weighted loss function methods for interactions of two-dimensional oceanic internal solitary waves (Q6130987) (← links)
• A Rate of Convergence of Weak Adversarial Neural Networks for the Second Order Parabolic PDEs (Q6143000) (← links)
• Learning Specialized Activation Functions for Physics-Informed Neural Networks (Q6143615) (← links)
• Bi-Orthogonal fPINN: A Physics-Informed Neural Network Method for Solving Time-Dependent Stochastic Fractional PDEs (Q6143622) (← links)
• Structure-preserving discretization of fractional vector calculus using discrete exterior calculus (Q6144189) (← links)
• Label-free learning of elliptic partial differential equation solvers with generalizability across boundary value problems (Q6146999) (← links)
• MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling (Q6151271) (← links)
• <tt>TNet</tt>: A Model-Constrained Tikhonov Network Approach for Inverse Problems (Q6154957) (← links)
• NON-DIFFERENTIABLE EXACT SOLUTIONS OF THE LOCAL FRACTIONAL ZAKHAROV–KUZNETSOV EQUATION ON THE CANTOR SETS (Q6159810) (← links)
• Deep learning soliton dynamics and complex potentials recognition for 1D and 2D \(\mathcal{PT}\)-symmetric saturable nonlinear Schrödinger equations (Q6160033) (← links)
• PINN training using biobjective optimization: the trade-off between data loss and residual loss (Q6162878) (← links)
• Neural Network Method for Integral Fractional Laplace Equations (Q6165528) (← links)
• Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (Q6171723) (← links)
• A learned conservative semi-Lagrangian finite volume scheme for transport simulations (Q6173366) (← links)
• An artificial neural network approach to identify the parameter in a nonlinear subdiffusion model (Q6177840) (← links)
• On the non‐differentiable exact solutions of the (2 + 1)‐dimensional local fractional breaking soliton equation on Cantor sets (Q6182154) (← links)
• Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression (Q6185193) (← links)
• A nonlinear-manifold reduced-order model and operator learning for partial differential equations with sharp solution gradients (Q6185246) (← links)
• Multi-output physics-informed neural network for one- and two-dimensional nonlinear time distributed-order models (Q6186170) (← 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)
• Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions (Q6202605) (← links)
• Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions (Q6204733) (← links)
• Adaptive loss weighting auxiliary output fPINNs for solving fractional partial integro-differential equations (Q6496475) (← links)
• Zero coordinate shift: whetted automatic differentiation for physics-informed operator learning (Q6497254) (← links)
• Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations (Q6497269) (← links)
• Correcting model misspecification in physics-informed neural networks (PINNs) (Q6497270) (← links)
• Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models (Q6537067) (← links)
• PDNNs: the parallel deep neural networks for the Navier-Stokes equations coupled with heat equation (Q6537439) (← links)
• A deep learning method for computing mean exit time excited by weak Gaussian noise (Q6539426) (← links)
• Gradient auxiliary physics-informed neural network for nonlinear biharmonic equation (Q6540123) (← links)
• Adaptive deep neural networks for solving corner singular problems (Q6545698) (← links)