Pages that link to "Item:Q5162370"

The following pages link to On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370):

Displaying 50 items.

• PPINN: parareal physics-informed neural network for time-dependent PDEs (Q2020276) (← links)
• Optimally weighted loss functions for solving PDEs with neural networks (Q2068635) (← links)
• Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449) (← 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)
• Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning (Q2077801) (← links)
• INN: interfaced neural networks as an accessible meshless approach for solving interface PDE problems (Q2083675) (← links)
• Solving PDEs by variational physics-informed neural networks: an a posteriori error analysis (Q2084593) (← links)
• Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545) (← links)
• A non-gradient method for solving elliptic partial differential equations with deep neural networks (Q2099748) (← links)
• Self-adaptive physics-informed neural networks (Q2112437) (← links)
• On stability and regularization for data-driven solution of parabolic inverse source problems (Q2112451) (← links)
• A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions (Q2135816) (← links)
• When and why PINNs fail to train: a neural tangent kernel perspective (Q2136450) (← links)
• Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Q2138842) (← links)
• Modeling, analysis and physics informed neural network approaches for studying the dynamics of COVID-19 involving human-human and human-pathogen interaction (Q2138895) (← links)
• Neural networks enforcing physical symmetries in nonlinear dynamical lattices: the case example of the Ablowitz-Ladik model (Q2140106) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• Learning generative neural networks with physics knowledge (Q2146912) (← links)
• A decision-making machine learning approach in Hermite spectral approximations of partial differential equations (Q2149019) (← links)
• Improved architectures and training algorithms for deep operator networks (Q2149522) (← links)
• The deep parametric PDE method and applications to option pricing (Q2161843) (← links)
• Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations (Q2162011) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• RPINNs: rectified-physics informed neural networks for solving stationary partial differential equations (Q2166581) (← links)
• Fractional Chebyshev deep neural network (FCDNN) for solving differential models (Q2169390) (← links)
• ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains (Q2222287) (← links)
• Adaptive activation functions accelerate convergence in deep and physics-informed neural networks (Q2223034) (← links)
• On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks (Q2237440) (← links)
• Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (Q2246919) (← links)
• Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (Q2671386) (← links)
• The deep learning Galerkin method for the general Stokes equations (Q2674271) (← links)
• Data-driven method to learn the most probable transition pathway and stochastic differential equation (Q2677788) (← links)
• Kolmogorov n-width and Lagrangian physics-informed neural networks: a causality-conforming manifold for convection-dominated PDEs (Q2678525) (← links)
• A deep first-order system least squares method for solving elliptic PDEs (Q2679352) (← links)
• Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
• A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs (Q2681136) (← links)
• Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations (Q2682670) (← links)
• Physics-informed neural networks for data-driven simulation: advantages, limitations, and opportunities (Q2683126) (← links)
• Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
• A deep Fourier residual method for solving PDEs using neural networks (Q2683430) (← links)
• A deep double Ritz method (\(\mathrm{D^2RM}\)) for solving partial differential equations using neural networks (Q2683471) (← links)
• Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN) (Q2687566) (← links)
• Control of partial differential equations via physics-informed neural networks (Q2696946) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (Q4958918) (← links)
• Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach (Q5031306) (← links)
• When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? (Q5043367) (← links)
• A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)