Pages that link to "Item:Q2127017"

The following pages link to NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations (Q2127017):

Displaying 50 items.

• A structure-preserving neural differential operator with embedded Hamiltonian constraints for modeling structural dynamics (Q6109265) (← links)
• Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
• HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions (Q6119293) (← links)
• Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems (Q6119307) (← links)
• Weak adversarial networks for solving forward and inverse problems involving 2D incompressible Navier-Stokes equations (Q6125404) (← links)
• Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
• Loss-attentional physics-informed neural networks (Q6126561) (← links)
• Physics-informed neural networks with two weighted loss function methods for interactions of two-dimensional oceanic internal solitary waves (Q6130987) (← links)
• SeismicNET: physics-informed neural networks for seismic wave modeling in semi-infinite domain (Q6137634) (← links)
• A novel temperature prediction method without using energy equation based on physics-informed neural network (PINN): a case study on plate-circular/square pin-fin heat sinks (Q6138011) (← links)
• Bi-Orthogonal fPINN: A Physics-Informed Neural Network Method for Solving Time-Dependent Stochastic Fractional PDEs (Q6143622) (← links)
• Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model (Q6144182) (← links)
• Label-free learning of elliptic partial differential equation solvers with generalizability across boundary value problems (Q6146999) (← links)
• PINN training using biobjective optimization: the trade-off between data loss and residual loss (Q6162878) (← links)
• Automatic boundary fitting framework of boundary dependent physics-informed neural network solving partial differential equation with complex boundary conditions (Q6171169) (← links)
• Adaptive transfer learning for PINN (Q6173323) (← links)
• Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks (Q6173359) (← links)
• Hard enforcement of physics-informed neural network solutions of acoustic wave propagation (Q6180100) (← links)
• Multi-level neural networks for accurate solutions of boundary-value problems (Q6185225) (← links)
• An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations (Q6191768) (← links)
• A new numerical approach method to solve the Lotka-Volterra predator-prey models with discrete delays (Q6197526) (← links)
• wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (Q6197777) (← links)
• Operator approximation of the wave equation based on deep learning of Green's function (Q6202600) (← links)
• Residual-based attention in physics-informed neural networks (Q6202991) (← links)
• I-FENN with temporal convolutional networks: expediting the load-history analysis of non-local gradient damage propagation (Q6497179) (← 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)
• The coupled physical-informed neural networks for the two phase magnetohydrodynamic flows (Q6549888) (← links)
• Anti-derivatives approximator for enhancing physics-informed neural networks (Q6550163) (← links)
• Dynamically configured physics-informed neural network in topology optimization applications (Q6550166) (← links)
• Revisiting tensor basis neural network for Reynolds stress modeling: application to plane channel and square duct flows (Q6566970) (← links)
• Applications of finite difference-based physics-informed neural networks to steady incompressible isothermal and thermal flows (Q6570872) (← links)
• Cell-average based neural network method for Hunter-Saxton equations (Q6578103) (← links)
• A causality-DeepONet for causal responses of linear dynamical systems (Q6584819) (← links)
• Adaptive sampling points based multi-scale residual network for solving partial differential equations (Q6585372) (← links)
• Multilevel domain decomposition-based architectures for physics-informed neural networks (Q6588267) (← links)
• Interface PINNs (I-PINNs): a physics-informed neural networks framework for interface problems (Q6588288) (← links)
• GMC-PINNs: a new general Monte Carlo PINNs method for solving fractional partial differential equations on irregular domains (Q6588348) (← links)
• A meshless solver for blood flow simulations in elastic vessels using a physics-informed neural network (Q6590130) (← links)
• Advanced physics-informed neural networks for numerical approximation of the coupled Schrödinger-KdV equation (Q6590978) (← links)
• Physics-informed deep learning of rate-and-state fault friction (Q6595877) (← links)
• Pseudo grid-based physics-informed convolutional-recurrent network solving the integrable nonlinear lattice equations (Q6599876) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← links)
• A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks (Q6609808) (← links)
• Binary structured physics-informed neural networks for solving equations with rapidly changing solutions (Q6615737) (← links)
• Option pricing in the Heston model with physics inspired neural networks (Q6630708) (← links)
• PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
• GFN: a graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications (Q6643617) (← links)
• MHDnet: physics-preserving learning for solving magnetohydrodynamics problems (Q6646462) (← links)