Pages that link to "Item:Q2138842"

The following pages link to Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Q2138842):

Displaying 50 items.

• Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs (Q2060092) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations (Q2072734) (← links)
• Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
• A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
• Physics-informed neural networks for gravity field modeling of small bodies (Q2104214) (← links)
• Neural eikonal solver: improving accuracy of physics-informed neural networks for solving eikonal equation in case of caustics (Q2112483) (← links)
• Parallel physics-informed neural networks via domain decomposition (Q2133497) (← links)
• Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks (Q2156788) (← links)
• RPINNs: rectified-physics informed neural networks for solving stationary partial differential equations (Q2166581) (← links)
• A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Q2314336) (← links)
• Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
• A-WPINN algorithm for the data-driven vector-soliton solutions and parameter discovery of general coupled nonlinear equations (Q2677793) (← 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)
• A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media (Q2681146) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions (Q2683577) (← links)
• Deep energy method in topology optimization applications (Q2694685) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Physics informed neural networks: a case study for gas transport problems (Q2699348) (← links)
• PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations (Q5045670) (← links)
• On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370) (← links)
• Multi-Fidelity Machine Learning Applied to Steady Fluid Flows (Q5880416) (← links)
• Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems (Q5887902) (← links)
• A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions (Q6048429) (← links)
• VC-PINN: variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient (Q6069931) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations (Q6078492) (← links)
• Phase-field DeepONet: physics-informed deep operator neural network for fast simulations of pattern formation governed by gradient flows of free-energy functionals (Q6084433) (← links)
• Development of POD-based reduced order models applied to shallow water equations using augmented Riemann solvers (Q6094702) (← links)
• A dimension-augmented physics-informed neural network (DaPINN) with high level accuracy and efficiency (Q6095102) (← links)
• Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations (Q6096508) (← links)
• Deep learning data-driven multi-soliton dynamics and parameters discovery for the fifth-order Kaup-Kuperschmidt equation (Q6096544) (← links)
• The robust physics-informed neural networks for a typical fourth-order phase field model (Q6103706) (← links)
• A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
• Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs) (Q6111307) (← links)
• A novel sampling method for adaptive gradient-enhanced kriging (Q6118505) (← links)
• Solving seepage equation using physics-informed residual network without labeled data (Q6120152) (← links)
• Loss-attentional physics-informed neural networks (Q6126561) (← links)
• Learning Specialized Activation Functions for Physics-Informed Neural Networks (Q6143615) (← links)
• Residual-based error correction for neural operator accelerated Infinite-dimensional Bayesian inverse problems (Q6147083) (← links)
• HRW: Hybrid Residual and Weak Form Loss for Solving Elliptic Interface Problems with Neural Network (Q6151336) (← links)
• Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread (Q6157162) (← links)
• Boundary-safe PINNs extension: application to non-linear parabolic PDEs in counterparty credit risk (Q6157931) (← links)
• PINN training using biobjective optimization: the trade-off between data loss and residual loss (Q6162878) (← links)
• A symmetry group based supervised learning method for solving partial differential equations (Q6171229) (← links)
• A learned conservative semi-Lagrangian finite volume scheme for transport simulations (Q6173366) (← links)
• Failure-Informed Adaptive Sampling for PINNs (Q6175124) (← links)