On the public citation graph

Time difference physics-informed neural network for fractional water wave models

https://doi.org/10.1016/j.rinam.2022.100347

0 signed · 49 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mma.6788openalex-snapshot

On fractional Kakutani–Matsuuchi water wave model: Implementing a reliable implicit finite difference method

inferredcites →https://doi.org/10.1007/bf01589116openalex-snapshot

On the limited memory BFGS method for large scale optimization

inferredcites →https://doi.org/10.1007/s10614-022-10279-xopenalex-snapshot

A Deep Learning Based Numerical PDE Method for Option Pricing

inferredcites →https://doi.org/10.1007/s10915-021-01630-9openalex-snapshot

The Unified Theory of Shifted Convolution Quadrature for Fractional Calculus

inferredcites →https://doi.org/10.1007/s10915-022-01879-8openalex-snapshot

Discovery of Subdiffusion Problem with Noisy Data via Deep Learning

inferredcites →https://doi.org/10.1007/s11075-020-01048-8openalex-snapshot

Fast second-order time two-mesh mixed finite element method for a nonlinear distributed-order sub-diffusion model

inferredcites →https://doi.org/10.1016/0893-6080(89)90020-8openalex-snapshot

Multilayer feedforward networks are universal approximators

inferredcites →https://doi.org/10.1016/j.apm.2014.03.051openalex-snapshot

Finite difference/spectral approximations to a water wave model with a nonlocal viscous term

inferredcites →https://doi.org/10.1016/j.apnum.2005.03.003openalex-snapshot

A fully discrete difference scheme for a diffusion-wave system

inferredcites →https://doi.org/10.1016/j.camwa.2015.11.018openalex-snapshot

Efficient numerical schemes for fractional water wave models

inferredcites →https://doi.org/10.1016/j.cma.2019.112789openalex-snapshot

Physics-informed neural networks for high-speed flows

inferredcites →https://doi.org/10.1016/j.compfluid.2022.105583openalex-snapshot

RPINNs: Rectified-physics informed neural networks for solving stationary partial differential equations

inferredcites →https://doi.org/10.1016/j.crma.2007.06.007openalex-snapshot

Viscous potential free-surface flows in a fluid layer of finite depth

inferredcites →https://doi.org/10.1016/j.ijheatmasstransfer.2017.12.118openalex-snapshot

Time two-mesh algorithm combined with finite element method for time fractional water wave model

inferredcites →https://doi.org/10.1016/j.jcp.2007.02.001openalex-snapshot

Finite difference/spectral approximations for the time-fractional diffusion equation

inferredcites →https://doi.org/10.1016/j.jcp.2018.08.029openalex-snapshot

DGM: A deep learning algorithm for solving partial differential equations

inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-snapshot

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

inferredcites →https://doi.org/10.1016/j.jcp.2020.109913openalex-snapshot

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

inferredcites →https://doi.org/10.1016/j.jcp.2021.110884openalex-snapshot

DeLISA: Deep learning based iteration scheme approximation for solving PDEs

inferredcites →https://doi.org/10.1016/j.jcp.2022.111260openalex-snapshot

A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations

inferredcites →https://doi.org/10.1016/j.matcom.2022.07.001openalex-snapshot

Fast high-order compact difference scheme for the nonlinear distributed-order fractional Sobolev model appearing in porous media

inferredcites →https://doi.org/10.1016/j.rinam.2022.100264openalex-snapshot

Efficient numerical algorithm with the second-order time accuracy for a two-dimensional nonlinear fourth-order fractional wave equation

inferredcites →https://doi.org/10.1016/j.rinam.2022.100339openalex-snapshot

Compact difference scheme for time-fractional nonlinear fourth-order diffusion equation with time delay

inferredcites →https://doi.org/10.1073/pnas.1718942115openalex-snapshot

Solving high-dimensional partial differential equations using deep learning

inferredcites →https://doi.org/10.1115/1.4050542openalex-snapshot

Physics-Informed Neural Networks for Heat Transfer Problems

inferredcites →https://doi.org/10.1137/18m1229845openalex-snapshot

fPINNs: Fractional Physics-Informed Neural Networks

inferredcites →https://doi.org/10.3390/fractalfract6040201openalex-snapshot

Second-Order Time Stepping Scheme Combined with a Mixed Element Method for a 2D Nonlinear Fourth-Order Fractional Integro-Differential Equations

inferredcites →https://doi.org/10.3934/dcds.2010.27.1473openalex-snapshot

Decay of solutions toa water wave model with a nonlocal viscous dispersive term

inferredcites →https://doi.org/10.3934/dcdsb.2020168openalex-snapshot

Approximation methods for the distributed order calculus using the convolution quadrature

inferredcites →https://doi.org/10.4208/jcm.2101-m2020-0342openalex-snapshot

Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4648080openalex-extracted

inferred← citeshttps://doi.org/10.1002/jnm.70073openalex-snapshot

Multilayer Neural Networks Enhanced With Hybrid Methods for Solving Fractional Partial Differential Equations

inferred← citeshttps://doi.org/10.1007/s11831-026-10563-1openalex-snapshot

Stochastic Solitary Waves and Noise-Driven Chaotic Transitions in the Kakutani–Matsuuchi Model: A Dynamical Perspective

inferred← citeshttps://doi.org/10.1016/j.amc.2024.129234openalex-snapshot

Numerical simulation of time fractional Allen-Cahn equation based on Hermite neural solver

inferred← citeshttps://doi.org/10.1016/j.camwa.2023.11.018openalex-snapshot

Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model

inferred← citeshttps://doi.org/10.1016/j.camwa.2026.01.019openalex-snapshot

ITPINN : Integral-trainable physics informed neural network for solving high-dimensional evolution non-local partial differential equations

inferred← citeshttps://doi.org/10.1016/j.cnsns.2025.108833openalex-snapshot

Numerical analysis and comparison of fractional physics-informed neural networks in unsaturated flow process

inferred← citeshttps://doi.org/10.1016/j.enganabound.2025.106418openalex-snapshot

dPINNs: A physics-informed framework for forward and inverse problems governed by distributed-order derivatives

inferred← citeshttps://doi.org/10.1016/j.enganabound.2025.106596openalex-snapshot

Physics-informed neural network combined with weighted square terms and two-phase training strategy for partial differential equations

inferred← citeshttps://doi.org/10.1016/j.ijheatmasstransfer.2023.124805openalex-snapshot

Parameter estimation for unsteady MHD oscillatory free convective flow of generalized second grade fluid with Hall effects and thermal radiation effects

inferred← citeshttps://doi.org/10.1016/j.neucom.2024.129134openalex-snapshot

Numerical solutions for space–time conformable nonlinear partial differential equations via a scientific machine learning technique

inferred← citeshttps://doi.org/10.1016/j.nhres.2026.07.003openalex-snapshot

Physics-informed neural networks and variants in weather and hydrological modeling: a systematic review

inferred← citeshttps://doi.org/10.1016/j.ocemod.2025.102601openalex-snapshot

Pre-trained physics-informed neural networks for one-dimensional wave propagation in coastal engineering

inferred← citeshttps://doi.org/10.1016/j.rinam.2024.100462openalex-snapshot

TDOR-MPINNs: Multi-output physics-informed neural networks based on time differential order reduction for solving coupled Klein–Gordon–Zakharov systems

inferred← citeshttps://doi.org/10.1016/j.rinam.2024.100493openalex-snapshot

A randomized neural network based Petrov–Galerkin method for approximating the solution of fractional order boundary value problems

inferred← citeshttps://doi.org/10.1016/j.rinp.2024.107615openalex-snapshot

Solitary wave solutions for the (1+1)-dimensional nonlinear Kakutani–Matsuuchi model of internal gravity waves

inferred← citeshttps://doi.org/10.3934/math.20251101openalex-snapshot

A forward progressive physics informed neural network with Taylor series expansion for solving evolution partial differential equations

inferred← citeshttps://doi.org/10.3934/math.2025716openalex-snapshot

New solitary solutions for stochastic Kakutani–Matsuuchi model of internal gravity waves

inferred← citeshttps://doi.org/10.3934/nhm.2023080openalex-snapshot

Multi-output physics-informed neural network for one- and two-dimensional nonlinear time distributed-order models

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.