Pages that link to "Item:Q5230662"

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

Displaying 44 items.

• Multi-scale time-stepping of partial differential equations with transformers (Q6550140) (← links)
• Solving the non-local Fokker-Planck equations by deep learning (Q6551384) (← links)
• Physics-informed machine learning in asymptotic homogenization of elliptic equations (Q6557811) (← links)
• The data-driven rogue waves of the Hirota equation by using mix-training PINNs approach (Q6558868) (← links)
• Speeding up and reducing memory usage for scientific machine learning via mixed precision (Q6566075) (← links)
• Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons (Q6569679) (← links)
• The Calderón's problem via DeepONets (Q6570540) (← links)
• Non-diffusive neural network method for hyperbolic conservation laws (Q6572177) (← links)
• Bright-dark rogue wave transition in coupled ab system via the physics-informed neural networks method (Q6574264) (← links)
• Exponential stability of fractional-order uncertain systems with asynchronous switching and impulses (Q6577239) (← links)
• A gradient-enhanced physics-informed neural networks method for the wave equation (Q6583846) (← links)
• Recovering the source term in elliptic equation via deep learning: method and convergence analysis (Q6586293) (← links)
• Variational temporal convolutional networks for I-FENN thermoelasticity (Q6588274) (← links)
• GMC-PINNs: a new general Monte Carlo PINNs method for solving fractional partial differential equations on irregular domains (Q6588348) (← links)
• E-PINN: extended physics informed neural network for the forward and inverse problems of high-order nonlinear integro-differential equations (Q6590587) (← links)
• Advanced physics-informed neural networks for numerical approximation of the coupled Schrödinger-KdV equation (Q6590978) (← links)
• Machine learning for nonlinear integro-differential equations with degenerate kernel scheme (Q6591000) (← links)
• Mikusiński's operational calculus for multi-dimensional fractional operators with applications to fractional PDEs (Q6591007) (← links)
• Data-driven rogue waves solutions for the focusing and variable coefficient nonlinear Schrödinger equations via deep learning (Q6592624) (← links)
• Deep neural networks for probability of default modelling (Q6593214) (← links)
• Bayesian inversion with neural operator (BINO) for modeling subdiffusion: forward and inverse problems (Q6593344) (← links)
• Physics-specialized neural network with hard constraints for solving multi-material diffusion problems (Q6595894) (← links)
• Solving partial differential equations by LS-SVM (Q6606433) (← links)
• Fractional weak adversarial networks for the stationary fractional advection dispersion equations (Q6612980) (← links)
• f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain (Q6614990) (← links)
• Solvability of nonlocal Hilfer fractional matrix boundary value problems with \(p\)-Laplacian at resonance in \(\mathbb{R}^n\) (Q6617564) (← links)
• A randomized neural network based Petrov-Galerkin method for approximating the solution of fractional order boundary value problems (Q6618294) (← links)
• Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of a time fractional equation (Q6630929) (← links)
• Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions (Q6631167) (← links)
• Novel localized wave of modified Kadomtsev-Petviashvili equation (Q6632888) (← links)
• Deep learning method for finding eigenpairs in Sturm-Liouville eigenvalue problems (Q6638598) (← links)
• Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks (Q6643609) (← links)
• Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning (Q6645133) (← links)
• MHDnet: physics-preserving learning for solving magnetohydrodynamics problems (Q6646462) (← links)
• A numerical approach for soil microbiota growth prediction through physics-informed neural network (Q6646501) (← links)
• Deep surrogate model for learning Green's function associated with linear reaction-diffusion operator (Q6648520) (← links)
• Simultaneous space-time Hermite wavelet method for time-fractional nonlinear weakly singular integro-partial differential equations (Q6649205) (← links)
• Improving weak PINNs for hyperbolic conservation laws: dual norm computation, boundary conditions and systems (Q6658817) (← links)
• I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN) (Q6661937) (← links)
• A stabilized physics informed neural networks method for wave equations (Q6662428) (← links)
• An immersed interface neural network for elliptic interface problems (Q6664871) (← links)
• A deep learning method for incompressible fluid equations and the coupling problems (Q6665361) (← links)
• Annealed adaptive importance sampling method in PINNs for solving high dimensional partial differential equations (Q6670734) (← links)
• Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs (Q6671950) (← links)