Pages that link to "Item:Q2222627"

The following pages link to PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network (Q2222627):

Displaying 50 items.

• The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation (Q5096451) (← links)
• MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs (Q5106291) (← links)
• Convolutional Neural Networks in Phase Space and Inverse Problems (Q5149211) (← links)
• Discovery of Dynamics Using Linear Multistep Methods (Q5151929) (← links)
• DL-PDE: Deep-Learning Based Data-Driven Discovery of Partial Differential Equations from Discrete and Noisy Data (Q5163201) (← links)
• Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems (Q5163887) (← links)
• NeuralPDE: modelling dynamical systems from data (Q6041301) (← links)
• Data-driven sparse identification of nonlinear dynamical systems using linear multistep methods (Q6042122) (← links)
• Deep learning neural networks for the third-order nonlinear Schrödinger equation: bright solitons, breathers, and rogue waves (Q6046358) (← links)
• The Inverse Heat Transfer Problem of Malan Loess Based on Machine Learning with Finite Element Solver as the Trainer (Q6048282) (← links)
• Simultaneous neural network approximation for smooth functions (Q6052416) (← links)
• PhySR: physics-informed deep super-resolution for spatiotemporal data (Q6054215) (← links)
• Physics-incorporated convolutional recurrent neural networks for source identification and forecasting of dynamical systems (Q6055145) (← links)
• Randomized neural network with Petrov-Galerkin methods for solving linear and nonlinear partial differential equations (Q6058946) (← links)
• Group projected subspace pursuit for identification of variable coefficient differential equations (GP-IDENT) (Q6087946) (← links)
• Model discovery of compartmental models with graph-supported neural networks (Q6090296) (← links)
• A Bayesian framework for learning governing partial differential equation from data (Q6090678) (← links)
• Deep-OSG: deep learning of operators in semigroup (Q6094763) (← links)
• Dosnet as a non-black-box PDE solver: when deep learning meets operator splitting (Q6095076) (← links)
• Deep Ritz method with adaptive quadrature for linear elasticity (Q6096475) (← links)
• A framework based on symbolic regression coupled with eXtended physics-informed neural networks for gray-box learning of equations of motion from data (Q6096490) (← links)
• Data-driven, multi-moment fluid modeling of Landau damping (Q6097331) (← links)
• Accuracy and architecture studies of residual neural network method for ordinary differential equations (Q6101548) (← links)
• A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
• Pseudo-Hamiltonian neural networks for learning partial differential equations (Q6119277) (← links)
• Pseudo-Hamiltonian system identification (Q6120390) (← links)
• Transferable neural networks for partial differential equations (Q6123346) (← links)
• Forward to the special topic on ``Solving differential equations with deep learning'' (Q6130975) (← links)
• A Review of Data‐Driven Discovery for Dynamic Systems (Q6131430) (← links)
• A Variational Neural Network Approach for Glacier Modelling with Nonlinear Rheology (Q6143617) (← links)
• Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons (Q6143642) (← links)
• Learning the nonlinear flux function of a hidden scalar conservation law from data (Q6145277) (← links)
• Quadrature rule based discovery of dynamics by data-driven denoising (Q6147082) (← links)
• Connections between numerical algorithms for PDEs and neural networks (Q6156049) (← links)
• Identification of the flux function of nonlinear conservation laws with variable parameters (Q6156252) (← links)
• \(\mathrm{U}^p\)-net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics (Q6159312) (← links)
• A priori error estimate of deep mixed residual method for elliptic PDEs (Q6182315) (← links)
• Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent? (Q6198233) (← links)
• On mathematical modeling in image reconstruction and beyond (Q6200218) (← links)
• Towards discovery of the differential equations (Q6204271) (← links)
• Learning the flux and diffusion function for degenerate convection-diffusion equations using different types of observations (Q6492247) (← links)
• A kernel framework for learning differential equations and their solution operators (Q6496499) (← links)
• Latent assimilation with implicit neural representations for unknown dynamics (Q6498490) (← links)
• Data-driven modeling of partially observed biological systems (Q6537200) (← links)
• Learning of discrete models of variational PDEs from data (Q6543708) (← links)
• Adaptive deep neural networks for solving corner singular problems (Q6545698) (← links)
• Data-driven models of nonautonomous systems (Q6553794) (← links)
• Theory and implementation of inelastic constitutive artificial neural networks (Q6566033) (← links)
• Gabor-filtered Fourier neural operator for solving partial differential equations (Q6566939) (← links)
• Deep learning-based method for solving seepage equation under unsteady boundary (Q6574145) (← links)