Pages that link to "Item:Q5230662"

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

Displaying 50 items.

• HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations (Q2172562) (← links)
• Discovering a universal variable-order fractional model for turbulent Couette flow using a physics-informed neural network (Q2173500) (← links)
• Physics-informed neural networks for high-speed flows (Q2175317) (← links)
• Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
• Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (Q2246919) (← links)
• Artificial neural network approximations of Cauchy inverse problem for linear PDEs (Q2247118) (← links)
• A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
• Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (Q2671386) (← links)
• Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
• The deep learning Galerkin method for the general Stokes equations (Q2674271) (← links)
• A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks (Q2679440) (← links)
• DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
• Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
• A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs (Q2681136) (← links)
• Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
• Physics-informed neural networks for data-driven simulation: advantages, limitations, and opportunities (Q2683126) (← links)
• Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
• Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions (Q2683577) (← links)
• Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization (Q2684140) (← links)
• Time difference physics-informed neural network for fractional water wave models (Q2690093) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
• Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks (Q5004999) (← links)
• Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements (Q5014838) (← links)
• DIFFUSION ON FRACTAL OBJECTS MODELING AND ITS PHYSICS-INFORMED NEURAL NETWORK SOLUTION (Q5024806) (← links)
• A New Artificial Neural Network Method for Solving Schrödinger Equations on Unbounded Domains (Q5045673) (← links)
• MIONet: Learning Multiple-Input Operators via Tensor Product (Q5048574) (← links)
• Convergence Rate Analysis for Deep Ritz Method (Q5077692) (← links)
• A Rate of Convergence of Physics Informed Neural Networks for the Linear Second Order Elliptic PDEs (Q5077701) (← links)
• Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs (Q5079535) (← links)
• Deep ReLU networks and high-order finite element methods (Q5132226) (← links)
• DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
• Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations (Q5162369) (← links)
• On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370) (← links)
• A cookbook for approximating Euclidean balls and for quadrature rules in finite element methods for nonlocal problems (Q5164242) (← links)
• Physics-Informed Neural Networks with Hard Constraints for Inverse Design (Q5165440) (← links)
• Analysis of Anisotropic Nonlocal Diffusion Models: Well-Posedness of Fractional Problems for Anomalous Transport (Q5885654) (← links)
• Numerical methods for nonlocal and fractional models (Q5887820) (← links)
• Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems (Q5887902) (← links)
• Time-dependent Dirac equation with physics-informed neural networks: computation and properties (Q6043079) (← links)
• Deep learning neural networks for the third-order nonlinear Schrödinger equation: bright solitons, breathers, and rogue waves (Q6046358) (← links)
• A neural networks-based numerical method for the generalized Caputo-type fractional differential equations (Q6047613) (← links)
• Efficient Monte Carlo Method for Integral Fractional Laplacian in Multiple Dimensions (Q6055561) (← links)
• On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures (Q6058580) (← links)
• Randomized neural network with Petrov-Galerkin methods for solving linear and nonlinear partial differential equations (Q6058946) (← 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 with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem (Q6062204) (← links)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• A general framework for substructuring‐based domain decomposition methods for models having nonlocal interactions (Q6069467) (← links)
• Efficient quadrature rules for finite element discretizations of nonlocal equations (Q6069468) (← links)