Pages that link to "Item:Q2123792"

The following pages link to nPINNs: nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications (Q2123792):

Displaying 49 items.

• Towards a unified theory of fractional and nonlocal vector calculus (Q2059216) (← links)
• Estimating the time-dependent contact rate of SIR and SEIR models in mathematical epidemiology using physics-informed neural networks (Q2071475) (← links)
• A data-driven peridynamic continuum model for upscaling molecular dynamics (Q2072501) (← links)
• On the prescription of boundary conditions for nonlocal Poisson's and peridynamics models (Q2080554) (← links)
• Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations (Q2083146) (← links)
• Variable-order approach to nonlocal elasticity: theoretical formulation, order identification via deep learning, and applications (Q2115574) (← links)
• Multifidelity modeling for physics-informed neural networks (PINNs) (Q2134766) (← links)
• When and why PINNs fail to train: a neural tangent kernel perspective (Q2136450) (← links)
• Meta-learning PINN loss functions (Q2139042) (← links)
• An optimization-based approach to parameter learning for fractional type nonlocal models (Q2147289) (← links)
• A decision-making machine learning approach in Hermite spectral approximations of partial differential equations (Q2149019) (← links)
• Efficient optimization-based quadrature for variational discretization of nonlocal problems (Q2156793) (← links)
• Efficient hybrid explicit-implicit learning for multiscale problems (Q2162007) (← links)
• Deep neural networks based temporal-difference methods for high-dimensional parabolic partial differential equations (Q2168314) (← links)
• A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
• Machine learning of nonlocal micro-structural defect evolutions in crystalline materials (Q2679512) (← links)
• A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs (Q2681136) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• Local parameter identification with neural ordinary differential equations (Q2690025) (← links)
• An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Tempered fractional LES modeling (Q5015104) (← links)
• Convergence Rate Analysis for Deep Ritz Method (Q5077692) (← links)
• A Rate of Convergence of Physics Informed Neural Networks for the Linear Second Order Elliptic PDEs (Q5077701) (← links)
• A NONLOCAL MODELING FOR SOLVING TIME FRACTIONAL DIFFUSION EQUATION ARISING IN FLUID MECHANICS (Q5101549) (← links)
• Numerical methods for nonlocal and fractional models (Q5887820) (← links)
• Efficient Monte Carlo Method for Integral Fractional Laplacian in Multiple Dimensions (Q6055561) (← links)
• Randomized neural network with Petrov-Galerkin methods for solving linear and nonlinear partial differential equations (Q6058946) (← links)
• A general framework for substructuring‐based domain decomposition methods for models having nonlocal interactions (Q6069467) (← links)
• Efficient quadrature rules for finite element discretizations of nonlocal equations (Q6069468) (← links)
• A fractional model for anomalous diffusion with increased variability: Analysis, algorithms and applications to interface problems (Q6090390) (← links)
• A data‐driven bond‐based peridynamic model derived from group method of data handling neural network with genetic algorithm (Q6092283) (← links)
• An extended physics informed neural network for preliminary analysis of parametric optimal control problems (Q6104895) (← links)
• An Asymptotically Compatible Coupling Formulation for Nonlocal Interface Problems with Jumps (Q6108168) (← links)
• Branched latent neural maps (Q6118560) (← links)
• A Rate of Convergence of Weak Adversarial Neural Networks for the Second Order Parabolic PDEs (Q6143000) (← links)
• MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling (Q6151271) (← links)
• PINN-FORM: a new physics-informed neural network for reliability analysis with partial differential equation (Q6171227) (← links)
• nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications (Q6338382) (← links)
• I-FENN with temporal convolutional networks: expediting the load-history analysis of non-local gradient damage propagation (Q6497179) (← links)
• Solving the non-local Fokker-Planck equations by deep learning (Q6551384) (← links)
• Reconstructing \(S\)-matrix phases with machine learning (Q6568203) (← links)
• The line rogue wave solutions of the nonlocal Davey-Stewartson I equation with \textit{PT} symmetry based on the improved physics-informed neural network (Q6571797) (← links)
• Learning about structural errors in models of complex dynamical systems (Q6572173) (← links)
• Machine learning for nonlinear integro-differential equations with degenerate kernel scheme (Q6591000) (← links)
• Higher-order multi-scale physics-informed neural network (HOMS-PINN) method and its convergence analysis for solving elastic problems of authentic composite materials (Q6633295) (← links)
• A stabilized physics informed neural networks method for wave equations (Q6662428) (← links)
• A least-squares Fourier frame method for nonlocal diffusion models on arbitrary domains (Q6663391) (← links)
• An immersed interface neural network for elliptic interface problems (Q6664871) (← links)