The following pages link to XPINNs (Q1350720):
Displaying 36 items.
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893) (← links)
- Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
- A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations (Q2072734) (← links)
- CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
- INN: interfaced neural networks as an accessible meshless approach for solving interface PDE problems (Q2083675) (← links)
- Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545) (← links)
- Schwarz waveform relaxation-learning for advection-diffusion-reaction equations (Q2106899) (← links)
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation (Q2106998) (← links)
- Data-driven prediction of soliton solutions of the higher-order NLSE via the strongly-constrained PINN method (Q2107164) (← links)
- Parallel physics-informed neural networks via domain decomposition (Q2133497) (← links)
- Hybrid FEM-NN models: combining artificial neural networks with the finite element method (Q2133536) (← links)
- Physics-informed neural networks for the shallow-water equations on the sphere (Q2133783) (← links)
- The mixed deep energy method for resolving concentration features in finite strain hyperelasticity (Q2134762) (← links)
- Multifidelity modeling for physics-informed neural networks (PINNs) (Q2134766) (← links)
- Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244) (← links)
- Thermodynamically consistent physics-informed neural networks for hyperbolic systems (Q2136443) (← links)
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (Q2138799) (← links)
- Physics informed neural networks for continuum micromechanics (Q2138812) (← links)
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Q2138842) (← links)
- Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training (Q2145138) (← links)
- HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations (Q2172562) (← 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)
- A finite element based deep learning solver for parametric PDEs (Q2670366) (← links)
- A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (Q2670380) (← links)
- A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
- On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements (Q2671403) (← links)
- Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
- ADLGM: an efficient adaptive sampling deep learning Galerkin method (Q2683243) (← links)
- Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions (Q2683577) (← links)
- When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? (Q5043367) (← links)
- PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations (Q5045670) (← links)
- Generalization Error Analysis of Neural Networks with Gradient Based Regularization (Q5045671) (← links)
- Convergence Rate Analysis for Deep Ritz Method (Q5077692) (← links)
- Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs (Q5079535) (← links)
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations (Q5162369) (← links)
