Pages that link to "Item:Q5150214"

The following pages link to DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214):

Displaying 50 items.

• Solving forward and inverse problems of the logarithmic nonlinear Schrödinger equation with \(\mathcal{PT}\)-symmetric harmonic potential via deep learning (Q822569) (← links)
• An immersed boundary neural network for solving elliptic equations with singular forces on arbitrary domains (Q1980025) (← links)
• Traveling wave solutions of partial differential equations via neural networks (Q1983171) (← links)
• Numerical solution of the parametric diffusion equation by deep neural networks (Q2049099) (← links)
• Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
• Deep learning of conjugate mappings (Q2077602) (← links)
• Data-driven discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes (Q2081273) (← links)
• CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
• Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations (Q2083146) (← links)
• Data-driven solutions and parameter discovery of the Sasa-Satsuma equation via the physics-informed neural networks method (Q2083739) (← links)
• A priori and a posteriori error estimates for the deep Ritz method applied to the Laplace and Stokes problem (Q2095152) (← 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)
• Optimal control of PDEs using physics-informed neural networks (Q2106939) (← links)
• Self-adaptive physics-informed neural networks (Q2112437) (← links)
• On stability and regularization for data-driven solution of parabolic inverse source problems (Q2112451) (← links)
• Neural eikonal solver: improving accuracy of physics-informed neural networks for solving eikonal equation in case of caustics (Q2112483) (← links)
• Robust modeling of unknown dynamical systems via ensemble averaged learning (Q2112552) (← links)
• DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks (Q2131084) (← links)
• SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs (Q2133032) (← links)
• Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation (Q2133495) (← links)
• DeepM\&Mnet for hypersonics: predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators (Q2133505) (← links)
• A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder (Q2134764) (← links)
• Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244) (← links)
• When and why PINNs fail to train: a neural tangent kernel perspective (Q2136450) (← links)
• Deep neural network modeling of unknown partial differential equations in nodal space (Q2136465) (← links)
• A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (Q2138799) (← links)
• Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Q2138842) (← links)
• Meta-learning PINN loss functions (Q2139042) (← links)
• IGA-reuse-NET: a deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization (Q2139715) (← 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)
• Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training (Q2145138) (← links)
• Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks (Q2156788) (← links)
• Lagrangian dual framework for conservative neural network solutions of kinetic equations (Q2158858) (← links)
• ReLU deep neural networks from the hierarchical basis perspective (Q2159911) (← links)
• Numerical approximation of partial differential equations by a variable projection method with artificial neural networks (Q2160472) (← links)
• Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations (Q2162011) (← links)
• Deep reinforcement learning of viscous incompressible flow (Q2162036) (← 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)
• Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning (Q2167994) (← links)
• Solving flows of dynamical systems by deep neural networks and a novel deep learning algorithm (Q2168118) (← links)
• Fractional Chebyshev deep neural network (FCDNN) for solving differential models (Q2169390) (← links)
• Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers \textit{via} the modified PINN (Q2169695) (← links)
• Hidden physics model for parameter estimation of elastic wave equations (Q2236961) (← links)
• Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (Q2236964) (← links)
• Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (Q2237458) (← links)